A Technical Review of Urban Land Use--Transportation Models as Tools for Evaluating Vehicle Travel Reduction Strategies
ORNL-6881
A Technical Review of Urban Land Use-Transportation
Models as Tools for Evaluating Vehicle Travel
Reduction Strategies
Frank Southworth
Center for Transportation Analysis
Energy Division
July 1995
Prepared for
the Office of Environmental Analysis
and Sustainable Development
U. S. Department of Energy
Prepared by
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
managed by
LOCKHEED MARTIN ENERGY SYSTEMS, INC.
for the
U. S. DEPARTMENT OF ENERGY
under contract DE-AC05-84OR21400
CONTENTS
Page
FIGURES v
EXECUTIVE SUMMARY vii
1. INTRODUCTION 1
1.1 URBAN TRAVEL GROWTH: WHY THE CONCERN? 1
1.2 PURPOSE OF THIS REVIEW 2
1.3 OVERVIEW OF THE TECHNICAL CHALLENGE 3
1.3.1 Conceptual Issues 3
1.3.2 Practical Issues 8
1.4 ORGANIZATION OF THE REVIEW AND MAJOR
CONCLUSIONS 10
2. INTEGRATED URBAN LAND USECTRANSPORTATION
MODELS 13
2.1 OVERVIEW 13
2.2 SURVEY OF EMPIRICALLY APPLIED MODELS 15
2.2.1 Survey of the Literature 15
2.2.2 Nature of Model Applications 19
2.3 MODELING THE URBAN TRANSPORTATION SYSTEM 20
2.4 LINKING TRANSPORTATION AND URBAN LAND USE
MODELS 27
2.4.1 The Lowry Model and Related Developments 30
2.4.1.1 Background 30
2.4.1.2 DRAM, EMPAL, and ITLUP 30
2.4.2 Normative Planning and Related
Mathematical Programming Developments 33
2.4.2.1 Background 33
2.4.2.2 The POLIS Model 35
2.4.3 Multisectoral Spatial Modeling Using
Input-Output Frameworks 41
2.4.3.1 Background 41
2.4.3.2 The MEPLAN Model 42
2.4.4 Contributions from Urban Economics 46
2.4.4.1 Background 46
2.4.4.2 Kim's Chicago Model 46
2.4.5 Uses of Micro-Analytic Simulation 50
2.4.5.1 Background 50
2.4.5.2 The MASTER Model 51
2.5 APPROACHES TO URBAN DYNAMICS 53
2.5.1 Background 53
2.5.2 The Dortmund Model 55
iii
3. USING INTEGRATED MODELS IN POLICY ANALYSIS: AN
ASSESSMENT 59
3.1 INTRODUCTION 59
3.2 MODEL VALIDATION ISSUES 60
3.3 THEORETICAL ISSUES: TOWARDS MORE REALISTIC
MODELS 63
3.3.1 Household Travel Mobility Modeling 63
3.3.1.1 Criticisms of The Traditional
Transportation Planning Model 63
3.3.1.2 Implications for Modeling Travel
Reduction Strategies 65
3.3.1.3 Some Recent Developments in
Travel Demand Modeling 66
3.3.2 Urban Goods Movement Modeling 70
3.3.3 Modeling Transportation's Continued Role
in Urban Development 72
3.3.3.1 Transportation Infrastructure
Investment Impacts 72
3.3.3.2 Spatial Agglomeration of
Activities 74
3.3.4 Simulation of Urban Dynamics 77
3.4 PRACTICAL ISSUES: TOWARDS MORE USABLE MODELS 80
REFERENCES 83
FIGURES
Page
1.Complexity of Functional Linkages in Urban System Dynamics 7
2.Integrated Modeling: General Schematic Flow Chart 14
3.Traditional Four-Step Urban Transportation Planning Model 21
4.Simple Two-Route, Two-Link Congested Traffic Assignment 26
5.Integrated Urban Modeling Showing Typical Submodels 29
6.Multi-Period,Recursive Simulation of Urban System Dynamics 54
v
EXECUTIVE SUMMARY
The continued growth of highway traffic in the United States
has led to unwanted urban traffic congestion as well as to
noticeable urban air quality problems. These problems include
emissions covered by the 1990 Clean Air Act Amendments
(CAAA) and 1991 Intermodal Surface Transportation Efficiency
Act (ISTEA), as well as carbon dioxide and related Agreenhouse
gas@ emissions. Urban travel also creates a major demand for
imported oil. Therefore, for economic as well as environmental
reasons, transportation planning agencies at both the state and
metropolitan area level are focussing a good deal of attention
on urban travel reduction policies. Much discussed policy
instruments include those that encourage fewer trip starts,
shortertrip distances, shifts to higher-occupancy vehicles or
tononvehicular modes, and shifts in the timing of trips from
themore to the less congested periods of the day or week. Some
analysts have concluded that in order to bring about
sustainablereductions in urban traffic volumes, significant
changes will benecessary in the way our households and
businesses engage indaily travel. Such changes are likely to
involve changes in theways we organize and use
traffic-generating andBattracting landwithin our urban areas.
The purpose of this review is to evaluatethe ability of current
analytic methods and models to supportboth the evaluation and
possibly the design of such vehicle travelreduction strategies,
including those strategies involving the reorganization and use
of urban land.
The review is organized into three sections. Section 1
describes the nature of the problem we are trying to model,
Section 2 reviews the state of the art in operational urban
landuseBtransportation simulation models, and Section 3
provides acritical assessment of such models as useful urban
transportationplanning tools. A number of areas are identified
where furthermodel development or testing is required. The
following is asynopsis of each section of the review.
Section 1 of the review describes the considerable technical
difficulties associated with identifying the causes and
directionsof urban traffic growth. It is concluded that to be
effective,transportation planning needs to bring together an
understandingof (1) how the transportation sector operates, (2)
how traffic-generating and attracting land is developed, (3)
how other technologies affect the demands for travel, (4) how
moderncompanies make their siting and site relocation
decisions, and (5)how the modern industrial lifestyles of
today's households affect,and are in turn affected by, each of
the above. Besides the complex conceptual issues involved,
challenging practical issues result from the need to handle
large amounts of spatially explicit data, and the need to
consider a wide range of possible, and sometimes competing
transportation control measures (TCM). Significant,
sustainable, and socially acceptable travel reduction
strategies will require careful multiyear land use planning.
Given the typical time lag between the opening of a major
transportation infrastructure or service and the
vii
subsequent land use response, interest is focussed in this
reviewon models capable of simulating policy impacts anywhere
from15 to 50 years into the future.
Section 2 reviews the current status of operational land
use-transportation planning models, and in particular the
development of Aintegrated@ urban analysis models. A listing
ofthe most commonly referenced models is provided. The key
theoretical and operational developments of the past 30 years
arediscussed, using the mathematical details from selected
modelingsystems to illustrate the range of approaches now
available forsimulating urban travel patterns and their
multiyear impacts. Taken as a set, current models have managed
to combine the entropy maximization and locational
accessibility premises that are the basis of spatial
interaction theory with economically rational notions of
utility maximization and consumer choice.From the urban
economics literature they have taken the idea of equilibration
between transportation demand and supply and linked it to a
residential market clearing process.Methodologically, they make
use of nonlinear mathematical programming methods,
interregional input-output methods, and the latest developments
in econometrics and microsimulation to model jointly the
demands for travel, residences, employment, services, and urban
land. The more comprehensive models also simulate demographic
changes in the urban population as well changes in physical
stocks other than transportation infrastructure, including
models of the aging and renewal process associated with the
urban housing market.
The key trait these models have in common is their ability
to feed back the expected results of adding new transportation
infrastructure or services, computed within a transportation
submodel, to a travel cost sensitive land use submodel. They
simulate urban dynamics by iterating the simulated urban system
through a series of discrete time intervals. Here the level of
sophistication varies considerably across models: from a simple
one-shot,30-year forecasting process, to recursive formulations
which move the urban system forward in time through a series of
successively updated, 1-to 5-year intervals. They model these
events using an extensive database, usually resulting in the
allocation of traffic volumes and speeds over detailed
link-node representations of multimodal urban transportation
networks. They have been used in a number of different
countries to simulate a range of travel reduction strategies,
including fuel and road pricing policies, the spatial
reallocation of traffic-generating land uses, and the
introduction of new highways and transit services.
However, despite advancing in a number of theoretical and
practical directions since Lowry's 1964 "Model of Metropolis,"
these models are only now finding their way into U.S. practice.
Past reticence to employ them has stemmed in part from their
analytic complexity, in part from their significant data
requirements and similarly significant demands on
computational resources. While today's desktop computers can
now provide much of the computing power required, the other
issues remain unresolved. Spurred on by the demands placed on
metropolitan planners by the CAAA and supporting ISTEA
legislation, these models are now receiving renewed scrutiny.
At the same time, recent
viii
empirical and theoretical developments suggest that current
models may need to be either adapted or replaced if realistic
simulations of traveler responses to travel-reduction
strategies are to be forthcoming. Here a difficulty facing
model assessment is the limited information available from
model validation exercises, a process exacerbated by the
extended time frames required to capture the true effects on
travel of the moresignificant land use changes.
Section 3 considers a number of frequently voiced criticisms
of currently operational models and recasts these perceived
weaknesses as candidate areas for further research. Many of
these criticisms are linked to continued use of the
traditional four-step urban transportation planning model, and
in particular,the persistence of a single-destination, single
trip-purpose-based approach to travel generation. There is a
widely recognized need to develop more effective ways to
capture nontraditional travel reduction options, such as
telecommuting and teleshopping, alternatively fueled but
perhaps limited-range vehicles, and nontraditional work weeks.
Improved "travel activity analysis" models under development
include the modeling of multidestination, multipurpose trip
chains; the simulation of private vehicle use by different
household members and types of households; and the simulation
of daily travel schedules which recognize the growing number of
noncommute, non-peak period trips which are taking place.
Similarly, our treatment of the urban goods movement process
lacks any underlying behavioral rationale and needs to be tied
to a more comprehensive understanding of company logistics
planning. Some recent developments in both personal and goods
movement modelingare referenced as useful starting points for
subsequent analysis.
Needed improvements to the land use modeling process are
also discussed. In particular, and despite the frequently
referenced polycentric nature of urban growth over the course
ofthis century, there has been a failure to come to terms with
the causal mechanisms underlying intraurban, notably suburban,
center growth. The urban economics literature,while extensive,
has so far contributed little in the way of operationally
implementable theories of urban development. Among other
barriers to understanding, outmoded notions of what constitutes
"basic" and "nonbasic"employment activity make it difficult to
identify the underlying causes of commercial and industrial
business location decisions. A reassessment of this
traditionaldistinction, already evident in a number of recent
modelingefforts, needs to be pursued in a more comprehensive
manner.
A second area of land use planning warranting further study
is a more normative, or design-based, approach to urban
activity center planning. This includes approaches centered on
transit-oriented development and pedestrian-and cycle-oriented
land usearrangements.
ix
Third, a gradual move towards more behaviorally realistic,
truly dynamical modeling approaches is discussed, based on
differential or difference equation forms and supported by
longitudinal data such as multiwave panel analysis of
empirically validated travel behaviors. If such dynamical
analysis can be combined with a better understanding of why and
how urban centers form, and how designs of mixed use activity
centers influence household and business travel patterns, we
would have the basis for more realistic, and perhaps eventually
prescriptive,travel activity pattern simulations.
Finally, these urban simulation models need to be placed
within today's highly interactive software environments. We
need to produce not only policy-relevant, but also
policy-usable analysis tools. Urban planning ought to be a
highly interactive, consensus-building process. Black box
models should be neither acceptable nor necessary. Models
should be placed withinspatially explicit decision support aids
taking advantage of the latest geographic information systems
and relational database technology to open up the planning
process to well-informed local and regional planners.
Ultimately, urban planning comes down to compromise and common
sense. Yet we would be taking considerable risk, as we have
often been forced to do in the past, if we were to assume away
the complexity associated with multiyear planning by selecting
travel policies based largely on professional intuition.
Simulation models are necessary if we are to understand the
consequences of trying to control future traffic growth, and a
degree of complexity in model design cannot be avoided.
x
1. INTRODUCTION
1.1 URBAN TRAVEL GROWTH: WHY THE CONCERN?
Highway transportation today accounts for some 22% of the
nation's annual energy consumption: 97% of it in the form of
petroleum-based fuels (Davis, 1994). The century has been one
of steadily growing demand for vehicular travel. Between 1970
and 1990 total vehicle miles of highway travel within the
United States grew at an average annual rate of 3.2% (Davis,
1994, Table 3.2). While some reduction in this rate of growth
may result from a saturation in vehicle ownership and license
holding, many experts expect urban travel to continue to
increase as a result of (1) significant population gains within
our largest cities (Downs, 1992), (2) a generally growing
interest in discretionary forms of nonwork travel (see Hu and
Young, 1994), and (3) our continued failure to develop
alternatives to low-occupancy vehicle use (Johnson, 1993).
One evident impact of this traffic growth has been urban
pollution. Mobile source emissions from the highway
transportation sector alone are estimated to account for some
70% of our society=s carbon monoxide generation, 39% of its
nitrogen dioxide, 30% of emissions of VOCs , and 28% of its
small particulate matter (PM-10) generation, along with
significant contributions also to nitrogen oxide and sulphur
dioxide emissions (Curran et al., 1992). Nor are these the
only emissions of interest. Increased atmospheric accumulations of
carbon dioxide and related tracegasesCnotably ozone, nitrous
oxide, methane, and chloroflourocarbonsCare today considered
by many scientists to be contributing to a "greenhouse effect,"
in which the level of heat retained within the planet's
atmosphere is causing global warming of the earth's surface.
As such concerns have passed from the scientific community into
wider public notice, interest in the amount of carbon dioxide
(CO2) resulting from motor vehicle use has begun to surface
with some regularity. It has been estimated that the
consumption of energy within the transportation sector
contributes some 32% of the nation's emissions of carbon
dioxide (Hillsman and Southworth,1990).
In addition to these now often-discussed "direct," or
vehicle miles of travel-based, estimates of fuel consumption
and emissions production, DeLuchi, Johnson, and Sperling (1987)
identified five additional, indirect sources of greenhouse
gases which result from the consumption of highway and other
transportation fuels. These are (1) end-use combustion of
fuels, including trucking of liquid transportation fuels to
retail outlets; (2) combustion of fuel in pipeline compressors
and pumps, and in barges and trains during wholesale
transmission of fuels to the distributor; (3) CO2 formed by the
chemical reactions of fuel synthesis; (4) CO2 formed by the use
of process energy in fuel
Volatile organic compounds, which along with nitrogen oxides
are precursors of ozone (o3)
1
2
production plants; and (5) combustion of fuel in the initial
extraction, preparation, and transportation of raw fuel
feedstock. To these sources we also need to add the emissions
generated in those processes used to build and maintain our
transportation infrastructure and its operating components,
including our roads, bridges, and vehicle and vehicle parts
manufacturing plants, and in the manufacture of the vehicles
themselves.
Air quality and fuel consumption are not the only public
policy issues, of course. Activities associated with the
transportation sector now cover a significant percentage of the
land in use within our cities. With infilling of development
between the major highway arteries that were the original
facilitators, if not progenitors of that growth, the need for
additional centers of activity besides the CBD emerged. The
result has been multicentered urban development in most large
cities and a consequent increase in suburb-to-suburb trips.
Many of these suburban centers are now suffering from their own
versions of traffic congestion and the losses of personal and
employee time that entails (see Orksi, 1985;JHK and Associates,
1989; Garreau, 1991; Southworth and Jones, 1995). And what
the ubiquitous automobile has done for personal mobility the
truck has done for the intraurban movement of goods, leading to
a growing number of instances of mixed truck-automobile
interaction, which raising additional issues of travel safety
as well as traffic congestion. A question now being asked is
where our overcongested cities will go from here. How can we
most cost-effectively deal with traffic growth and traffic
congestion in a socially as well as environmentally sound
manner?
1.2 PURPOSE OF THIS REVIEW
Spurred on by this interest, this review focusses on the
extent to which current theories and supporting methodologies
are sufficiently developed to be used (a) to help urban
planners assess the impacts of transportation plans and
policies which support the evolution of more energy-efficient
and less polluted cities, and (b) to aid in the design of
specific travel-reduction strategies.
Given the complexity of the subject, methods are here
synonymous with models: conceptual, mathematical, and for
practical purposes, computer-based. For almost four decades
now, we have been using computer-based urban transportation
planning models to improve our assessment of current travel
activity patterns and to predict future transportation
infrastructure needs in support of steadily growing automobile
and truck traffic. For the purposes of longer-range
forecasting, in the 15- to 50-year range, such transportation
planning models need to be tied to a broader-based land use
plan for the same region. Often, such land use plans are
themselves the result, at least in part, of a modeling
exercise. As our cities have grown, the relative advantage of
locations within them has changed because of the growing
demand for goods and services, the buildup in traffic
congestion, and the further development of the transportation
system in response to both of these forces. That is, the ease
or cost of travel between locations in turn
3
contributes to the economic vitality of specific business
enterprises, as well as to the desirability of specific
residential locations. With the passage of time, changes in
transportation costs may in turn cause a change in land use.
Linking transportation and land use planning exercises is
therefore a natural step in both the physical and the
subsequent economic planning process. The current status of
Aintegrated@ urban land use-transportation models is the
central topic of this review.
Of particular interest is the ability of such integrated
models to provide useful inputs to the selection of travel-
reduction strategies that will result in a net reduction in
aggregate fuel use and emissions. Such reductions are usually
thought of as resulting from one or more of the following five
outcomes:
1. a reduction in the number of trip starts;
2. a reduction in the length of individual trips, through
changes in destination;
3. a shift to either nonvehicular or higher-occupancy
modes of travel; and/or
4. a reduction in the amount of travel during the
congested, or "peak," commuting periods
5. a reduction in trip length and/or traffic congestion,
through changes in route.
In this review the strategies we are most interested in
are those that can maintain such travel reductions over a
period of years, and thus improve urban lifestyles
collectively as well as individually. For this reason also,
the sort of planning orizons we are interested in, and those
also best suited to the types of models reviewed below, cover
the 15- to 50-year time frame, although many of the processes
modeled may express themselves (and be simulated to do so) with
much shorter cycles. The emphasis of the review, the reader
should note, is on applicability of current methods, and not on
the applicability of specific travel-reduction strategies per
se.
1.3 OVERVIEW OF THE TECHNICAL CHALLENGE
1.3.1 Conceptual Issues
It is important first of all to note the complexity of the
relationships we are seeking to simulate. The role
transportation plays in the multiyear development of urban
systems remains far from clear. This is due in part to the
often long lag times between the introduction of a new highway,
rail line, or travelterminal and the subsequent effects on
surrounding businesses and residents. To date, our ability to
track such changes in a comprehensive manner has been a cost we
have generally not been willing to accept. What is clear is
that many different factors work simultaneously to shape our
cities. While the root causes of travel growth are found in
the development of urban land-what it is used for and how
intensively it is used-we are currently much less certain about
the subsequent effects of transportation system changes on land
use, and hence, in turn,
4
on longer-run travel patterns (Giuliano, 1989; TRB, 1991;
Kitamura, 1994). Public policies intended to produce
sustainable forms of energy-efficient and environmentally
acceptable travel must encompass a better understanding of the
broader topic of urban land use, and in particular, of the way
transportation and other forms of urban land use feed back on
one another. Demonstrating the difficulties we face in
unravelling causes and effects, Zimmerman, West and Kozlowski
(1974) separated traffic which occurs on a new or newly
expanded highway into the following classes quoted by Kitamura,
1994):
. existing traffic,
. natural-growth traffic-due to traffic arising from
demographic or socioeconomic changes,
. diverted traffic-traffic from other streets and
highways,
. transferred traffic-traffic from other travel modes,
. shifted traffic-traffic going to new destinations,
. induced traffic-"new" trips encouraged by the
presence of the new highway, and
. development traffic-traffic generated by land-use
changes.
In recent years the opening or widening of a major stretch
of urban highway has frequently resulted in rapid traffic
growth. The road seems to fill up in a surprisingly short
period of time, then settles down to a new and generally higher
level of daily use. In areas where the demand for additional
road space has been building for some time, much of this new or
induced trip making by businesses or households may be an
expression of the latent demand for greater access to
opportunities which already existed within the system. Recent
evidence suggests that within U.S. cities over one million in
population, such latent demand may represent as much as 13% of
any new travel induced by a highway capacity expansion (Rathi
et al., 1991).
In addition to the above effects, temporal changes in trip
making may also influence the picture. Greater ease of travel
may induce some freight as well as personal travel to shift to
the now less congested highway, possibly into the peak period of
use. Many cities in recent years have experienced a temporal
spreading of such peak congestion periods, in what is a
collective expression of trip departure timing adjustments in
the face of congestion delays. Certainly, experts are often at
odds on the likely effects of adding new highway capacity in a
given situation (see Deakin, 1991).
As with highways, the full impacts of introducing new
transit infrastructure also remain far from clear. Over the
past three decades a number of studies of the effects of
introducing new or improved rail service, and to a lesser
extent new bus or trolley lines, have been carried out.
Armstrong (1994) summarizes the results of the best-known U.S.
studies (see also Dehghani and Harvey, 1994; Hunt, McMillan,
and Abraham,1994). The
5
findings appear to support the notion that property values
suffer in the immediate vicinity of intraurban rail stations,
and possibly (though less conclusively) along rights-of-way but
that rents may increase on average within the communities
supporting heavy rail or commuter rail transitstations.
However, results are far from consistent across studies,
except that any tendencies toward more compact urban growth or
higher urban densities appear to be offset by larger forces
towards urban decentralization (Deakin, 1991; Giuliano, 1989).
Giuliano (1986a), Mackett (1994), and Pisarski (1994) each
briefly review a number of past highway and transit investment
programs and their attributed impacts on subsequent urban
development and land prices. While the transportation-land use
feedback effects do often occur, current evidence and
understanding does little to clarify the situation for the next study
to be carried out. What is clear is the need to use models
which recognize the above complexities if we are to unravel the
conundrum posed by transportation infrastructure-initiated
urban development.
In trying to model not only the more immediate mode and
route shifts but also the longer-term relationships between
transportation and other forms of urban land use (notably
destination shifted, induced, and development traffic) we are
dealing with both a large number and a wide variety of activity
types, decision makers, and underlying motives for action.
While urban residents have chosen in growing numbers to move
outward from the city centers in search of more space at lower
rents, most commercial and industrial land users still seek the
economies of scale associated with spatial proximity to similar
and complementary employment activities. With the onset of the
information society, a third important trend is the emergence of
locationally indifferent, or Afootloose,@ service and
information- based companies which are no longer tied to the
location of key resource inputs or local markets for their
products. We therefore have at least three very different
types of locational activity operating within our urban areas.
Making the situation more complex, the locational decisions
within each of these residential and employment activity
sectors ultimately impact each other. Where workers live
determines the available labor pool, and where residents work
affects their choice of residence. Choice of residence in turn
affects the size of the consumer market for service and retail
products.
Further complicating the situation, the very urban stage
on which we are trying to apply our models has been shifting
quite rapidly of late. As a society we are undergoing some
significant changes not only in the way we travel but also
in the way we communicate and indeed live with each other. New
urban lifestyles are emerging as new forms of urban household
take shape (see Putman, 1994, for a discussion related to
recent land use modeling experience). Similarly, the role to
be played in future cities by the ongoing telecommunications
revolution is far from clear (US DOT, 1993; Greene, Hillsman,
and Wolfe 1994). To be useful planning tools our models must
be capable of incorporating different assumptions about the
effects of today's emerging travel and communications
technologies on the ways we interact with one another, both
within and between future cities.
6
As a minimum then, effective transportation planning
must bring together an understanding of (1) how the
transportation sector operates, (2) how traffic-generating and-
attracting land is developed, (3) how other technologies affect
the demands for travel, (4) how modern companies make their
siting and site relocation decisions, and (5) how the modern
industrial lifestyles of today's households affect, and are in
turn affected by, each of the above.
Figure 1 shows the sort of complexity we are trying to
come to terms with, if we take up the challenge of trying to
simulate, in any reasonable detail, the multiyear impacts of
urban transportation plans. Transportation demand and supply
considerations are shown at the center of a readily and rapidly
expandable series of interconnected causes and effects. Demands
for new and better transportation services are shown as
resulting from changes in the utilization of urban land. The
travel cost changes which result from providing new
transportation services cause activity pattern shifts which in
turn affect the local economy (i.e., the revenue generated by
the purchase of goods and services at specific sites). These
changes in turn affect local employment, which in turn affects
local demographics. Changes in employment and population
affect demand for services (of all kinds) which can either
create new businesses or cause businesses to close down with
loss of their competitive advantage. These sociodemographic
changes also affect local housing prices and eventually the
need for new housing starts. With new business ventures and new
residential neighborhoods come new demands for travelCand the
cycle begins again.
Shown at various locations within Figure 1 is the
potential for federal, state, metropolitan and local
governments to influence urban activity patternsCnotably
through transportation service pricing and capacity control,
through urban land utilization and labor supporting policies,
and through environmental legislation. Within the United
States this includes the use of private/public sector
partnerships in the development of local services. Also shown
in Figure 1 are a number of "other factors" involved in both
the transportation and land use decision-making processes of
each of the actors involved. These include the regionwide and
nationwide adoption of cost, time, and labor saving
technologies, including the advances in automotive engine
design and alternative fuels technology which have been the key
sources of travel-related fuel and emissions reductions to
date.
Adding the need to understand the energy related
environmental impacts of particular transportation system
developments further complicates the matter. Shape, size,
density of development, and the spatial dispersion of
activities have all been found to influence transportation
energy requirements. However, even highly abstract studies
7
Click HERE for graphic.
8
energy-efficient land use patterns quickly throw up
complexities which cloud interpretation of results (see Owens,
1989), while past empirically based modeling efforts leave
considerable room for uncertainty of cause and effect (see
Southworth and Jones, 1995). The actual outcome for fuel use,
and in particular for emissions, of specific spatial
arrangements of activities is, again, far from clear. What
such studies demonstrate is the underlying complexity of the
issues involved in the development of energy efficient and
environmentally clean cities. As Pisarski puts it (TRB, 1991),
"The attempt to express, much less understand, the nature of
the relationships inherent in transportation, urban form and
the environment is a great challenge. Analysis can be
overwhelmed by the inextricable linkages between them, each
shaping, and shaped by, the others."
1.3.2 Practical Issues
The above is a summary of the many conceptual issues involved
in urban land use-transportation interactions over time.
On a more pragmatic note, simulating the real behavior of a
complex urban system also requires the manipulation of
substantial detail, what Harris (1983) has called the Acentral
dilemma@ with regard to our design and construction of policy
useful urban planning models. This detail is required because
metropolitan transportation plans are by their nature spatially
explicit. They are involved at the fully urban scale with
planning for hundreds of thousands of travelers and hundreds of
firms. Depending on the model used, developing such plans
requires the availability of substantial amounts of spatially
referenced data, including socioeconomic-demographic data,
network structure data, travel cost data, and possibly housing,
commerical, and industrial stock data as well as data on land
rents and other factor prices. Indeed, past models have to a
significant degree been used to apply theory to fill gaps in
existing travel survey and land use inventory data.
As a corollary to this situation, the possible policy actions
are also very numerous, and quite varied. Consequently, the
attempt to configure policy sets out of combinations of these
actions can quickly lead to a combinatorial explosion. The
necessary detail required to accommodate such policy analyses
within suitably comprehensive model-based tools can, if not
cleverly controlled, become quite staggering.
Table 1 lists many commonly cited transportation control
measures (TCMs) as they correlate with the five general types
of travel-reduction strategies listed in Section 1.2 (see, for
example, Boyce et al., 1981; Ferguson, 1990; Euritt et al.,
1994). A concern clearly evident within recent literature is
that many of these TCMs are only stopgap measures or
short-term solutions to the larger questions of how our cities
ought to evolve (see Giuliano, 1992; Bae, 1993). The search
for cleaner and more fuel-efficient futures may require more
radical assessments of our current position. Among the TCMs
listed in Table 1 the most promising for sustainable reductions
in travel appear to be associated with the following:
9
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10
(1) more efficient urban land use arrangements;
(2) different forms of travel pricing policies (road tolls,
parking charges, high-
occupancy-vehicle fare subsidies, fuel and/or emissions
taxes);
(3) how best to use the latest developments in low-cost
information processing and telecommunications
technology as both a substitute for and a complement to
the movement of people and goods.
Within this review, considerations 2 and 3 are subsumed
under the notion of a more comprehensive view of Aintegrated
land use-transportation modeling@ which encompasses responses
to pricing policies and to real-time information systems as
part a broader, multiyear analysis of urban lifestyles and
business practices. Writing more generally about systems
modeling for public policy, Simon (1990, p. 7) sums up the
significant technical challenge we face as follows: "We must
separate what is essential from what is dispensable in order
to capture in our models a simplified picture of reality
which, nevertheless, will allow us to make the inferences that
are important to our goals."
A review of urban land use-transportation modeling can
therefore be viewed as an assessment of how well we have
managed to build such a picture. This is the perspective
within which the rest of the review is framed. To get a proper
picture we will certainly need to model it. How fine-grained a
picture we need to create in order to produce policy-sensitive
and sensible models remains a difficult question, but is
arguably the most important technical question we need to
answer.
Finally, it is important to remember that urban planning is
typically a multijurisdictional affair, involving local
metropolitan, as well asCwhere transport is concernedCregional
and federal decision-making. Effective modeling must take
place within such institutional arrangements and is subject,
like the rest of the planning process, to the reigning
institutional priorities. This suggests the development of
flexible and highly interactive decision support tools which
intimately involve planners at all levels in the policy
analysis process.
1.4 ORGANIZATION OF THE REVIEW AND MAJOR CONCLUSIONS
The rest of this review is organized as follows. Section 2
describes the current state of the art in operational,
integrated urban land use-transportation modeling. This
includes a review of the major theoretical and methodological
underpinnings of both the transportation and land use
components of such modeling systems, as well as a brief
summary of current best practice. Section 3 summarizes the
strengths and weaknesses of existing approaches as reflected in
recent commentaries within the literature. While real
11
progress has been made over the past three decades, and many
advances have passed from one modeling system to another, a
number of important weaknesses remain. Just how significant
current weaknesses are for policy analysis is currently
difficult to assess, given relatively limited application of
the majority of these modeling systems in actual planning
practice. Reasons for the limited application of these
models to date are discussed. As detailed forecasting tools,
the current models are entirely acceptable, even in the hands
experienced users. However, the nonintuitive results which
such model-based exercises regularly throw up suggest that they
represent a necessary component of future planning practice.
To better replicate traveler responses, however, more
behaviorally explicit models appear to be necessary if we are
to achieve greater realism.
We also need to recognize that such models, in their
software manifestations, are most useful as aids to scenario
generation and plan robustness testing, rather than as detailed
forecasting tools. By taking advantage of today's low-cost and
high-speed computers, we have the opportunity to move into a
new generation of urban transportation planning methods which
place planners within a highly interactive, multimedia-based
approach to the development of strategically focused and
incrementally adaptable urban transportation plans. Here, the
strategic role of models is to look for the errors that may be
associated with planner intuition, especially the errors which
can result from single or limited objective and perhaps myopic
policymaking.
2. INTEGRATED URBAN LAND USECTRANSPORTATION MODELS
2.1 OVERVIEW
This section of the report is devoted to a review of
operational integrated urban land use-transportationCmodels,
that is, models which have been empirically applied in either a
research or actual planning context within the past decade. The
term Aintegrated@ implies a feedback mechanism of the type
shown in Fig. 2, between the transportation system and the rest
of the urban land use system. Here the "land use' system
supplies the transportation system with estimates of the
location and volume of travel generators. "Land use" is a
general term here, covering both the types and intensities of
activities taking place at specific urban sites as well as the
physical area of land and any built structures used in support
of such activities. This involves modeling the demand for
employment, residential, shopping, and other activities at
different sites, and then translating and possibly constraining
these demands on the basis of appropriate physical or
artificial (i.e., planner-imposed) land utilization rates. The
more ambitious models also include the simulation of housing
stocks and floor space requirements for industrial buildings.
Within some models this also means simulation of pricing
effects on, in particular, residential choice. A further
extension in a limited number of modeling systems is a linked
simulation of demographic change, allowing the urban area's
population to evolve along with the evolution of the physical
city within which it lives and works. Wegener (1994) refers to
these types of model as "integrated urban models," although
the interaction between transportation and other land uses
remains their key trait.
The spatial distributions of residents and workers are
assumed to create the major demands for travel which drive
development of the transportation system. The "transportation"
system in Fig. 2 represents both the physical infrastructure and
services provided by the different travel modes, either
separately or in combination, as well as these demands, now
translated into mode-specific vehicular and nonvehicular trips,
for either passenger or freight movement. This interplay
between travel demand and supply resolves itself within the
typical transportation model into a series of single-purpose
and single- destination trips which together form the
on-the-road traffic volumes of interest to an environmental
analysis of fuel use and mobile source emissions.
The origin-to-destination travel costs resulting from this
interplay between transportation demand and supply can be fed
back into the residential and employment activity location
models, where they are used to allocate the area's residents
and workers to specific urban zones within the land use model.
This allows transportation system
13
14
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15
changes to affect land utilization, which in turn feeds back
its effects in the form of new levels (and locations) of
here plays a central role in all currently operational models.
As an integral component of such accessibility, travel cost
changes become part of the mechanism used to reallocate labor,
residents, retail and service activities, and when modeled,
freight flows between spatially separated land uses.
In terms of an urban dynamic, most models employ static-
recursive approaches to multiyear forecasting or (more
realistically) scenario generation. That is, cross-sectional
representations of the urban system are moved forward through a
series of discrete time intervals. However, both the
operational details and level of sophistication imposed on this
dynamic vary considerably across existing models. This is the
topic for Sect.2.5 below. A common planning horizon for such a
single- time-period forecast is 5 years, although intervals
from 1 year to as many as 30 years have been used. Forecasting
further into the future, an obviously risky business, is
accomplished in the more advanced modeling systems by iterating the
land use and transportation subsystems through a series of
discrete time intervals. In an effort to keep transportation
and other urban land uses in some kind of synchronization, both
lagged and marginally incremental methods are used to update
and to control for selected variables as part of this process.
Figure 2 also shows the location of three types of public
policy instruments commonly used to simulate the effects of
significant travel reduction strategies: (1) land use controls,
(2) fuel pricing policies, and (3) those transportation control
measures which impact directly the capacity and level of service
of the specific transportation modes.
2.2 SURVEY OF EMPIRICALLY APPLIED MODELS
2.2.1 Survey of the Literature
Table 2 lists the better-known and documented
operational models, along with some of their applications to
specific urbanized area studies. The table draws heavily on
the models reported by the International Study Group on Land
Use- Transportation Interaction (ISGLUTI) (Webster, Bly and
Paulley, 1988), on the survey of available models by Cambridge
Systematics and The Hague Consulting Group (1991), and on the
Coordinated through the British Transport and Road Research
Laboratory . tje OSGLUTI effort carried out comparisons of nine
different land use-transportations models using data from cities
in seven different developed countries(Annerstookt in
Netherlands; Tokyo an Osaka in Japan; Dortmund in Germany;
Leeds in the England; Bilbao in Spain; Uppsala in Sweden; and
Melborne in Australia). Subsequent work has extended these
model comparisons to (a) the application of more than one
model to the same city (see Wegener, Mackett and Simmonds,1991)
and (b) the application of the same model to more than one city
(Mackett) 1991 b). This sort of coordination is now being
continued through the SIGI working group within the World
conference on transportation Research
16
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17
reviews by Berechman and Gordon (1986), Berechman and
Small (1988), Mackett (1985), Putman (1983, 1991), and
Wegener (1994, 1995b). These sources were supplemented by a
further literature search and through contacts with a number of
the field=s leading model developers.
Among the most recent round of empirically supported
U.S. studies of note are those for the Chicago area (Anas and
Duann, 1986; Boyce et al., 1992, 1993; Kim, 1989); for the
San Francisco Bay Area (Prastacos, 1986a,b; Caindec and
Prastacos, 1995); the Puget Sound Region of Washington State
(Watterson, 1993); and Portland, Oregon=s Land Use,
Transportation, and Air Quality (LUTRAQ) study (Cambridge
Systematics C Hague Consulting Group, 1991). The most
widespread application of a particular modeling approach in the
United States comes out of the extensive model development and
calibration efforts of Putman and colleagues (see Putman 1983,
1991) , whose joint implementations of the Disaggregate
Residential Allocation Model (DRAM) and the Employment
Allocation Model (EMPAL) are currently used in some fourteen
of the largest U.S. metropolitan planning agencies (Putman,
1994). During the 1970s and 1980s Putman also developed the
Integrated Transportation Land Use Package (ITLUP), linking
DRAM and EMPAL with selected components of the traditional
four-step transportation planning model, containing submodels to
estimate trip distribution, modal choice,and traffic assignment.
References to past empirical applications of ITLUP include
studies in Kansas City, Washington, D.C., and Houston. The
LUTRAQ study also recommended use of an ITLUP-like
approach (Cambridge Systematics et al., 1992b). Recently
DRAM and EMPAL have been linked to the TRANSPLAN suite
of transportation planning models in a 772-zone application to
the southern California region, centered on Los Angeles (Putman,
1994).
Building on the CATLAS model of combined residential
location, housing and mode choice, the modeling of non-work
travel choices and commerical real estate markets in the New
York region (the NYSIM model), and the modeling of
metropolitan housing market dynamics in a number of US cities
(the CHPMM model), Anas and colleagues have developed a
highly integrated economic model of transportation and land use
called METROSIM. METROSIM (Anas, 1994) consists of 7 sub-
models, providing analysis of a region's basic industry, non-
basic industry, residential and commercial real estate, vacant
land, households, commuting and non-commuting travel and
traffic assignment, within a single, jointly solved-for
structure, that is strongly oriented towards theoretically sound
and empirically workable economic relationships.
I n the United Kingdom notable efforts to develop land
use-transportation models are found in both the theoretical and
the empirical work begun by Wilson and colleagues at the
University of Leeds (see Wilson et al., 1977, 1981), and carried
on by Mackett at the University College, London. Mackett has
devoted considerable effort to building and calibrating both the
Leeds Integrated Land Use-Transportation modeling package
(LILT) (Mackett 1983, 1991a,b) and the MASTER
microsimulation-based modeling system (Mackett, 1990b).
Echenique and colleagues at the University of Cambridge, and
subsequently within the commercial sector, have been especially
energetic in developing
18
and applying the MEPLAN modeling system. Their work includes
planning applications for the city of Bilbao, Spain, and for the
Third World cities of Sao Paulo, Brazil (Echenique, 1985);
Caracas, Venezuela (Feo et al., 1975); and central Chile (de
la Barra et al., 1975). Hunt and Simmonds (1993) reference 22
different empirical applications of MEPLAN, including a recent
study for Naples, Italy (Hunt, 1994). A similar but now
separate modeling system, TRANUS, has also been applied to the
la Barra, 1989), as well as to more idealized simulations of
energy and urban form relationships (de la Barra and Rickaby,
1982; Rickaby, 1987, 1991). Johnston (1995) indicates that
TRANUS is currently being experimented with in in Sacramento, at
the University of California at Davis, where it is being
examined in conjunction with the California Urban Futures Model,
or CUFM (see Landis, 1994).
Pioneering work in the field has also resulted from a
long-term involvement in the area by the Commonwealth
Scientific and Industrial Research Organization (CSIRO) in
Australia, based largely on use of the TOPAZ modeling system
(Brotchie, 1969; Brotchie, Dickey, and Sharpe, 1980; Sharpe,
1978, 1980, 1982 and other references therein). As its name
implies (Technique for Optimal Placement of Activities in
Zones), the approach taken in the TOPAZ model is a normative
one, using a very general location-allocation modeling system
adaptable to a number of different scales of spatial analysis.
In recent work, the object-oriented SUSTAIN model (Roy and
Marquez, 1993) is being developed to facilitate more idealized,
less idiosyncratic comparisons of different energy-efficient
forms of urban transportation infrastructure development.
Other work of interest in Australia includes development
of the LAND gaming-simulation model by Young and colleagues
at Monash University in Melbourne (Gu et al., 1992), and the
proposed PIMMS (Pricing and Investment Model for Multi-
Modal Systems) model, described by Hensher et al. (1993) at the
University of Sydney.
In Canada, initial progress in the development and
empirical application of an integrated land use-transportation
model to the Hamilton Consolidated Metropolitan Area is
reported by Anderson et al (1994), Kanargolou et al (1995) and
Anderson, Kanargolou, and Miller (1994). Here the early focus
has been placed on simulating automobile fuel consumption and
emissions.
In Japan, integrated urban modeling includes the
CALUTAS model (Computer-Aided Land Use Transport
Analysis System) (Nakamura et al., 1983) and the Osaka model
(Amano et al., 1985). Wegener (1994) briefly references other
recent Japanese developments. Other non-U.S. studies include
van Est's (1979) modeling of the Eindhoven urban area;
Bertuglia et al.'s (1981) modeling of Turin and Rome in Italy;
and a number of modeling applications to the city of Stockholm
in Sweden, including application of the Transportation and
Location, or TRANSLOC, model listed in Table 2 (see Boyce
and Ludqvist, 1987, for example).
19
For the Middle East, Garnett (1980) reports a planning
model and policy application for Tehran, Iran. Martinez
(1992a,b) recently calibrated his own version of an integrated
land use-transportation model for Santiago, Chile. Finally,
Cambridge Systematics and The Hague Consulting Group (1991)
also report the existence of two commercially available, ITLUP-
like computer packages known as TRACKS and TRANSTEP,
with 51 reputedly different applications in Australia and the
Far East.
2.2.2 Nature of Model Applications
As a set, the models listed above have been empirical by
applied to a wide range of policy questions. While the initial
reasons for developing the various modeling approaches may
have differed, the ISGLUTI study found sufficient similarity
across nine of the models reported in Table 1 to carry out a set
of common tests. These tests covered the effects on travel
choices and land use arrangements from introducing changes in
the following variables:
. population,
. land use restrictions,
. employment location policies,
. the location of retail (shopping) facilities,
. the costs of travel,
. mode-specific travel speeds and network structure,
. the timing of transport investment, and
. general economic climate (economic recession,
narrowed income distribution).
Moving into specific policy impact studies, Mackett
(1994) concludes that current models can be particularly useful
for analyzing either congestion reduction or energy reduction
strategies. (Also considered were safety, the environment,
social equity, quality of life, public expenditure and
privatization policies.) He lists the following commonly
available (if not always popular) public policy instruments as
being well suited to analysis with models which integrate
transportation planning decisions into a broader and longer-
range analysis of land use.
. restrictions of peripheral urban development,
. increases in the gasoline tax,
. increases in public transportation subsidies,
. increases in investments in public transportation
infrastructures,
. increases in transportation system (supply)
management,
. increases in transportation demand management, and
. introduction of road pricing schemes.
20
In a research context a handful of past studies have also
used such models to look specifically at alternative, if rather
abstract, energy-efficient urban futures (see Sharpe,1978, 1980,
1982; de la Barra and Rickaby, 1982; Rickaby, 1991; Roy and
Marquez, 1993). In the United States the 1990 Clean Air Act
Amendments and supporting legislation within the 1991
Intermodal Surface Transportation Efficiency Act have caused
recent practice to focus on using such models to forecast future
levels of urban air quality (see Putman, 1994, using the
DRAM/ITLUP modeling approach; and Watterson, 1993, using
modified versions of the DRAM and EMPAL models within the
Puget Sound Council of Governments model). The spatial as
well as temporal extent of such applications also varies, from
specific highway or transit corridor analyses to full-scale
urban area or complete transit network simulations.
2.3 MODELING THE URBAN TRANSPORTATION SYSTEM
Urban transportation modeling began in earnest in the
mid-1950s in the United States (see Weiner, 1992, for historical
developments). Since the 1960s most metropolitan areas have
used variants of the Urban Transportation Planning System
(UTPS) models shown in Fig. 3. This four-step, single-
destination, separable-purpose, daily trip-based approach has
dominated the transportation modeling literature. This includes
its use within the integrated land use-transportation models
listed above. It has been used to address a wide range of
issues covering the physical, economic, and ( in recent years)
energy and environmental impacts of major highway or rapid
transit investments. The approach is sequential in order to
avoid some very difficult multicollinearity problems found to
affect more direct estimation techniques. It is also meant to
be iterative in order to bring the transportation costs computed
within the trip distribution (= destination), modal choice, and
traffic assignment (routing) submodels down to a common set of
values.
In this system the urbanized area is first divided up into a
set of spatially contiguous traffic-generating and attracting
zones. For our largest cities this involves definition of
dozens, sometimes hundreds, of zones linked to highway and
transit networks containing hundreds, sometimes thousands, of
link and node records. The computational process can be started
with a simple all-or-nothing assignment of traffic to least-cost
interzonal travel paths. This can be done before any actual
trip volumes are Aloaded @ onto the network. Land use, when
modeled explicitly, comes into the process through its influence
on trip generation rates. Alternatively, daily trip frequencies
are estimated directly from zonally based population and
employment forecasts. These
A Summary of the transportation programs and provisions of the
CAAA has been written by the Federal Highway Administration
(FHWA, 1992a). Summaries both of the complete ISTEA, and of
its air quality programs and provisions are also provided by
the Federal Highway Administration (FHWA, 1992,c).
21
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22
forecasts are suitably disaggregated by household type or
economic sector based on significantly different observed
averaged trip rates. The trip generation (and trip attraction)
models are usually regression based, or built on category
analytic techniques (see Douglas and Lewis, 1970/71; Institute
of Traffic Engineers, 1987).
For a set of zone-specific, average daily trip originations,
one or more trip distribution models are then used to allocate
purpose-specific trips to destinations within the remaining set
of urban analysis zones. Within these spatial interaction
models the concept of locational accessibility to opportunities
plays a central role in the allocation process. If travel is a
derived demand, then accessibility is the "good" it provides.
Such locational accessibility indices have the form
Click HERE for graphic.
The use of spatial interaction models in urban planning
studies gained a boost with the elaboration of both entropy
maximizing (Wilson, 1967, 1970) and utility maximizing
(Neidercorn and Bechdolt, 1969; Golob, Gustafson and
Beckmann, 1973) theories, which have provided, respectively, a
more robust statistical mechanics/ information theoretic basis
and a rational economic basis for spatial interaction theory.
Subsequent theoretical efforts to link these two approaches
during the 1970s and 1980s have further strengthened the hold of
"logit"forms of interaction model on the discipline (see Anas,
1983a; Brotchie et al., 1979; Williams, 1977; Wilson, et al.,
1981). Such a logit model can be stated as
Click HERE for graphic.
23
This is a popular form of origin, demand or "production"
constrained spatial interaction model, which Wilson (1971)
placed within a family of possible models, including destination
(supply, Aattraction@)-constrained as well as demand and supply
(Adoubly@)-constrained forms. The issues of why and when we
travel are handled within this framework by incorporating
disaggregations by trip purpose and time of day, respectively.
This usually leads to separate matrices of zone-to-zone flows
coming out of a work trip model and one or more types of
nonwork trip (e.g., shopping, social and recreational, school
trip) distribution models.
The modal choice submodel Asplits@ these interzonal trip
volumes across the most likely travel modes (usually auto versus
public rail or bus transit, but with walk, cycle or multimodal
trips also possible). The logit is again the most popular form
in use. At this step Adisaggregate@Cthat is, individual
traveler Cresponse-based multinomial logit models have also
become popular in the United States, using McFadden's (1974)
maximum likelihood method to include a wide range of
explanatory variables as well as multiple travel choices within
such model calibration efforts.
A subsequent and now increasingly used theoretical
development was the specification of "nested" logit forms, which
allow the results from one production-or attraction-constrained
logit model to be passed into another in a behaviorally
consistent manner (see Williams, 1977; Ben-Akiva and Lerman,
1985). For example, mode m-specific travel (dis)utilities,
cijm (i.e., modal travel costs), can be averaged into a
destination choice model such as Eq. (2) above, using log-sum
or inclusive value terms of the form
Click HERE for graphic.
Click HERE for graphic.
Doubly constrained spatial interaction models have been popular
as journey-to-work models where a planning agency has census
data or other means of producing what it considers reasonably
accurate estimates of zonally based trip productions and
attractions.
24
which, in terms of equation (1) above is a log-accessibility
measure, and which in economic terms is often interpreted as a
locational or consumer=s surplus measure associated with zone i
(see Williams, 1977; Fisk and Boyce, 1984).
The resulting mode-specific interzonal traffic volumes are
then assigned to one or more routes, or paths, by the traffic
assignment submodel shown in Fig. 3. This results in a new set
of interzonal travel costs which ought to be submitted back to
the trip distribution model. The process of model calibration
should then be continued by iterating the travel costs within
the various mode, destination, and assignment submodels until
they converge to a single set of values.
A number of variants on this iterative procedure are now
used (see Boyce, Lupa, and Zhang, 1994). At the traffic
assignment stage the auto trips and any truck trip matrices that
have been generated are converted into passenger car equivalent
traffic volumes before being simultaneously loaded onto the
highway network. Logits can also be used to select alternative
routes and have been incorporated within a number of different
assignment methods (see Sheffi, 1985). However, the most
commonly referenced assignment model is the capacity-sensitive
approach proposed by Wardrop (1952). Under this approach,
which is geared to handling the congested conditions experienced
during the commute to and from work, urban traffic volumes are
distributed such that all multilink routes used between any
origin- to-destination pair of traffic zones have the same
travel time, while all available but unused routes have a higher
travel time. The result is termed a user optimal equilibrium
assignment in which no traveler can change his or her route
without incurring extra en route delays (Beckmann, McGuire, and
Winsten, 1956). Mathematically, this can be stated as
subject to
Click HERE for graphic.
where Tij is a trip matrix, and we are solving for fa = the flow
of traffic on link a. Here Ca(fa) = the congestion-sensitive
cost of travel along link a, such as a convex function of
25
Click HERE for graphic.
Figure 4 shows a simple two-route, two-link example for
the type of link speed-volume relationships often used in
practice. The area created under these two marginal link
travel cost curves is the solution to the objective function
given by Eq. (6) above. Efficient computational procedures now
exist for solving this and similar capacity-constrained traffic
assignment problems for quite large and detailed urban area
networks. Recent developments by Janson (1991) and Janson and
Southworth (1992) have also extended this sort of equilibrium
assignment model into computationally tractable dynamic forms,
which may soon allow the analysis of such strategies as
staggered work trip departure times and their effects on traffic
congestion. Such developments also take us squarely into the
realm of Intelligent Transportation Systems (ITS) research, an
area currently receiving large amounts of funding from the U.S.
Department of Transportation (DOT) in support of the 1991
ISTEA legislation.
Rail transit options are usually modeled over their own,
separate network. Where bus transit is a significant
alternative, passenger car equivalent (pce) conversion factors
can be used to simulate the effects of each bus within the
resulting traffic stream, and suitable network coding techniques
can handle the presence of bus-only lanes or other forms of
high-occupancy vehicle (HOV) facility. A similar pce procedure
can also be used to portray the effects of larger trucks in the
traffic stream.
Variants on this same four-step transportation system
modeling process are often used for both long-term (10-to 30-
year) planning, and shorter range (1-to 5-year) transportation
system management (TSM) planning (see Yu, 1982, for an
overview). In some cases specifically designed variants on the
overall modeling approach have been developed to better focus
on a particular TSM strategy; these include the Network
Performance Evaluation Model developed for the U.S.
Department of Energy (DOE) to analyze the energy and
environmental impacts of various types of HOV lanes, (see
Janson, Zozaya-Gorostiza, and Southworth, 1987).
Fuel use and related mobile-source, pollutant-specific
emissions estimates are typically computed using these
assignment model-generated traffic volumes and speeds. For this
purpose baseline emissions estimates for light-duty motor
vehicles (automobiles and light trucks) are generated by the
Federal Test Procedure. Under the FTP vehicles go
26
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27
through a series of stops and starts with an average driving
speed of 19.6 mph. Emissions rates for vehicles at other speeds
are derived by a statistical regression of fuel consumption
against average speedfor cycles other than the FTP. Speed
correction factors (SCFs) for this purpose have been developed
by the U.S. Environmental Protection Agency and by the
California Department of Transportation.
However, these emissions outputs, and the traffic
volumes themselves, are usually aggregated or averaged over one
or more traffic analysis zones for the purposes of computing
emissions on a wider regional or Agridded@ basis (see Quint and
Loudon, 1994; Outwater and Loudon, 1994). Currently, there is
a good deal of uncertainty surrounding the accuracy of
emissions calculations for carbon monoxide (CO), hydrocarbons
(HC), and oxides of nitrogen (NOX) and, in particular, their
relationship to actual traffic conditions (see Guensler, 1993;
Bae, 1993). Nor were the traffic volumes and speeds from static
traffic assignment models meant to handle such details. While
detailed traffic simulation programs based on individual vehicle
movements are now also in use, it has been only recently, and in
a research context, that this sort of detailed traffic flow
modeling has been tied directly to emissions estimation (see
Matzoros and Van Vliet, 1992a,b), and little testing of its
accuracy has been carried out.
For the purposes of estimating areawide CO2 emissions,
which are highly correlated with total fuel used, less concern
for such accuracy may be warranted. Unlike the CAAA-controlled
pollutants, which are by volume comparatively marginal engine
emissions, CO2 emissions are highly correlated with fuel used
and associated, congestion-conditioned vehicle miles traveled
(VMT). Nor need we be concerned within such an analysis of
greenhouse gas buildup with such location-specific issues as the
health effects of CO hotspots.
2.4 LINKING TRANSPORTATION AND URBAN LAND
USE MODELS
Urban land use modeling also began in the 1950s, again in the
United States (see Batty, 1980, who dates such efforts from
1958). Most of today=s operational land use-transportation
models derive from ideas and model forms introduced into the
wider literature during the 1960s and 1970s. There is now an
extensive literature dealing with the theoretical and
methodological as well as operational aspects of such models.
The discussion presented below draws on the historical and
technical accounts and efforts at synthesis described in, among
others, Anas (1984), Batty and Hutchinson (1983), Berechman
and Gordon (1986), Berechman and Small (1988), Bertuglia et
al. (1987), Echenique and Williams (1980), Echenique (1985),
Kim (1989), MacGill and Wilson (1979), Mackett (1985, 1994),
Putman (1983, 1991, 1994), Transportation Research Board
(1990), Wegener (1994, 1995b), Wilson (1987), and Wilson et al.
(1977, 1981).
28
Figure 5 shows the basic idea behind linking a land use
model to the four-step transportation planning model described
above. As noted in this figure, a number of modeling systems
use the spatial interaction formulas at the heart of their
residential and employment location submodels to replace
(obviate the need for) a separate set of trip-based distribution
models. The ITLUP model can be used to generate such trip
distributions within the DRAM submodel. The MEPLAN and TRANUS
models generate all of their inter-zonal flow matrices as a
series of Atrades@ within the land use modeling system. Within
a number of operational models, including the MEPLAN and Kim
models described later in this review, the urban system is
modeled as a series of markets, with emphasis placed on clearing
a transportation market and one or more other land use markets,
by solving endogenously for a suitable set of spatially varying
market prices; which include travel costs and site rents.
Within the less inclusive models, such as ITLUP, which avoid
endogenous modeling of nontransportation price mechanisms, an
equilibrium between the transportation system=s demands and
supplies can also be brought about; this also stabilizes the
parameters within the residential and employment activity
location submodels. Such considerations of equilibrium in urban
evolution quickly take us into the area of temporal dynamics.
Within the ITLUP, MEPLAN, and Dortmund models described
in some detail below, lagged effects play an important role in
linking different submodels within the transportation and land
use systems both across as well as within a single time period
(see Sect. 2.5, below).
While operational transportation planning models have
tended to be built around the above four-step approach, once we
link these developments to urban land use models a good deal
more variety is evident. At least five significantly different
theoretical and/or methodological approaches have combined to
produce the current state of best practice among such extended
and Aintegrated@ modeling systems. Each of these
approachesCthe Lowry model, normative and mathematical
programming developments, spatial input-output analysis, urban
economics, and microanalytical simulationCis reviewed briefly
below.
In the discussion of each of these approaches a model
from Table 2 has been selected for detailed presentation, as a
means of demonstrating how such developments translate into
current modeling practice. The reader should note, however,
that the assignment of a model below to a particular approach is
somewhat arbitrary. The order of presentation was selected to
show how current models have brought developments from a
number of the above discussed advances into their frameworks.
A significant feature of model advances over the past 30 years
has been the gradual incorporation and unification of different
theories and methods within individual modeling frameworks. The
purpose of the following descriptions is not to fully elaborate
on any single modeling system but to use specific models to
elaborate on key areas of development. In selecting examples for
presentation there is also a strong bias towards U.S.-based
modeling efforts. For a complete list of a model's current
functionality the reader should see the references cited in the text.
29
30
2.4.1 The Lowry Model and Related Developments
2.4.1.1 Background
Most operational urban land use models today, and all of
those discussed below, can trace their beginnings to Lowry=s
(1964) AModel of Metropolis@ for the city of Pittsburgh. The
original Lowry model incorporates the spatial distribution of
population, employment, retailing (the entire service, or "non-
basic," sector), and land use within a compact iterative
procedure requiring only nine equations and three inequalities.
In essence, the approach consists of linking together two
spatial interaction models. One of these models allocates
workers to a predefined set of land use zones on the basis of
exogenously supplied basic employment levels (i.e., employment
in manufacturing and primary industries). The dependent
families of these workers are then defined using a suitable
activity ratio (the ratio of total regional population to total
regional employment). These workers and their families demand
services, and these demands are met by means of a second spatial
interaction model which allocates this service supply, in the
form of "nonbasic" employment, across the same spatial zoning
system. Iteration is required to then bring the resulting
residential and nonbasic employment activity allocation models
into line with each other. To generate estimates of either land
area occupied or floor space used within each zone a two-stage
process is required. First, the residential and employment
activity levels are allocated across the set of available
zones, then suitable activity-to-floor space rates are assigned,
with checks to ensure that the physical limits and any planning
restrictions on the space within a zone are not violated.
2.4.1.2 DRAM, EMPAL, and ITLUP
In the United States the most used successors to Lowry=s
model are the Disaggregate Residential Allocation Model
(DRAM) and the Employment Allocation Model (EMPAL) as
developed by Putman and colleagues (see Putman, 1983, 1991).
Both are now in use in a number of U.S. cities (a recent count
was 14; Putman, 1994). On the basis of empirical testing Putman
(1983, Ch.7) specified DRAM to have the form
Click HERE for graphic.
31
Click HERE for graphic.
A similar level of elaboration has gone into development
of a number of service employment location models. Putman
(1983) provides the following formula for EMPAL:
Click HERE for graphic.
Click HERE for graphic.
which is interpreted within this and most spatial interaction
models as the inverse of a spatial accessibility index of the
Hansen type (Hansen, 1959) (recall Eq. 1).
Putman (1994) discusses the recent experience of regional and
metropolitan planning agencies with these iteratively linked
models, which require an additional subroutine or submodel to
translate their activity allocations into suitable zonal land
utilization rates. He notes that income group quartile and
quintile disaggregations (the latter matching trip generation
model groupings) are most common within DRAM; but that
ethnicity may be at least as useful a component in residence
selection within some of our larger cities. He also discusses
possible lagged variable forms of DRAM as a means for
improving next period forecasts of zonal populations by income
group (Eq. 10 above). Similarly, within EMPAL a number of
employment sectors may be defined, for example, based on
Standard Industrial Classification (SIC) groupings.
32
The first operational and truly AIntegrated@
Transportation Land-Use Package (ITLUP) in the United States
appears also to have been developed by Putman (see Putman,
1983, 1991) to provide a feedback mechanism between DRAM,
EMPAL, and the mode split and traffic assignment components
of the UTPS model described in Sect. 2.3. First EMPAL
allocates employment across analysis zones in the forecast time
period (period t) using prior period (t ! 1) accessibility,
population, and employment totals. A typical sectoral
breakdown might be two industrial (heavy and light), one basic
nonindustrial, and one nonbasic (e.g.,retail) sector (Webster et
al., 1988). These are typically 5-year forecasts. DRAM next
forecasts the future allocation of households using prior period
(t-1) locational accessibilities but also using the forecast
period t distribution of zonal employment.
A third submodel, actually within DRAM and termed
LANCON, calculates land consumption in the forecast period by
combining base year data with a forecast based on multiple
regression. DRAM also contains the system's trip distribution
models, by converting the housing allocation probabilities into
vehicle trips using region-specific vehicle utilization rates.
Three trip matrices are produced: home-to-work, home-to-shop,
and work-to-shop trips. The home-to-work trip matrices are then
split into private and public vehicular modes using a
multinomial logit model, and private trips are allocated to the
highway network using one of at least four available types of
capacity- constrained traffic assignment (see Putman, 1983,
991). Travel cost changes are fed back into the residential and
employment allocation models, which in turnCand subject to
suitable physical capacity or other planning constraints on
zonal land useCwill then generate new interaction matrices as a
result of revised locational accessibility measures.
Over the years Putman and colleagues have explored a
number of variations on this static-recursive approach to
forecasting (Putman, 1983, 1984, 1991). Miller (1990) also
describes a number of different approaches to this recursive
modeling process and provides a matrix formulation for ITLUP
which mirrors Garin's (1966) matrix formulation of the original
Lowry model.
In a recent study for PSCOG, Watterson (1993) also
describes the results of linking modified versions of DRAM and
EMPAL (see Watterson, 1990) to the widely used UTPS
software. This study is notable for its use of widely available
modeling packages, as well as an interesting description of
their application to a highly visible public planning study,
beset with real-world problems and deadlines. The process of
generating alternative scenarios used was, however, a much
simpler one: first set basic employment levels, then for the
year 2020 create a baseline set of travel costs and run DRAM and
EMPAL, then create scenario-specific sets of transportation s
ystem improvements for 2020, rerun DRAM and EMPAL, and then
rerun the UTPS travel models. Scenario-specific results are
then compared to the 2020 baseline model run. That is, a series
of alternative 30-year scenarios (1990B2020) are generated
within a single feedback loop. Attention was given to
environmental concerns, including the
33
simulation of regionwide mobile source emissions estimates.
Scenarios developed included application of a wide range of TCM
strategies to different forms of polynucleated urban
development.
An advantage of the DRAM/EMPAL-based approaches is
their basis in generally available data sources. This emphasis
also translates into a weakness of the approach: the absence of
any mechanism for simulating the land market clearing process
underlying multiyear infrastructure change. Clearly, more
comprehensive simulation of the land market requires additional
data that is generally difficult to collect, notably data on
the pricing of land, housing, and other forms of development.
An unresolved issue is how effectively we can generate multiyear
land use-transportation plans without incorporation of such
additional details.
2.4.2 Normative Planning and Related Mathematical
Programming Developments
2.4.2.1 Background
A second and equally consistent line of advance has
resulted from a normative approach; reflecting a long held
interest within the planning profession for best possible
solutions. Emphasis on prediction of future outcomes, or indeed
the replication of current or past ones, is replaced here by
efforts to define, or to Adesign,@ more efficient urban
futures. This viewpoint brings with it at least three
advantages: (1) it can make use of generally simpler
mathematical forms that are readily tied to theories of system
efficiency, cost minimization, or net gain; and therefore (2)
it avoids the need to account for a wide range of empirically
observed idiosyncrasies, while (3) using mathematical
programming frameworks to state the urban land use-
transportation problem as a single, if rather complex,
mathematical formulation.
From early beginnings in the use of linear programming
models of residential location (see Herbert and Stevens, 1960;
Harris, 1965), in which the Abid-rent function@ (see Sect.
2.4.4 below) made its operational appearance, an important step
forward came with the recognition that spatial interaction
models could also be written as convex programming problems
which could themselves be embedded within activity-allocation
modeling frameworks (see Wilson et al., 1981, for an extensive
technical treatment; also Erlander, 1977.)
The resulting urban Alocation-allocation models@ usually
take the form of convex mathematical programs subject to a set
of linear planning constraints. For example, an interesting
rearrangement of Eq. (1), using the logit/entropy maximizing
form of travel cost function, gives
Click HERE for graphic.
34
Click HERE for graphic.
These and related discoveries led researchers in a number
of countries to use the mathematical programming approach to
pursue alternative formulations of interrelated facility location-
allocation problems. This includes the prolific work in the
United
35
Kingdom by Wilson and colleagues ( MacGill and
Wilson, 1979; Wilson et al. 1977, 1981; see also the review by
Wilson, 1987), in Italy, by Leonardi (1979) and Bertuglia and
Leonardi (1980); in Sweden (see Boyce and Lundqvist, 1987), in
Australia (see Brotchie, et al.,1980; Sharpe, 1978, 1980, 1982,
using the TOPAZ model), and in Canada (Los, 1979). Similar
efforts are well represented in the United States. The
mathematical model proposed by Boyce and Southworth (1979),
for example, embeds each of Wilson's singly constrained, doubly
constrained, and unconstrained spatial interaction models
within a single programming framework which recognizes
different population subgroups on the basis of the temporal
stability in their residence and/or employment location. Boyce
and Southworth's Aquasi-dynamic@ formulation also incorporates
traffic route assignment and mode split within a single
optimization framework. The incorporation of further
components of the residential, employment, and travel choice
decisions within a single jointly optimized modeling framework
has been extensively studied in recent years by Boyce and
colleagues in Illinois, working with Chicago area data (see
Boyce 1988; Boyce et al., 1983, 1992, 1993). Two related
mathematical programming-based models to have been applied
empirically within the United States are Kim's Chicago area
model (Kim, 1989), used in a research context and Prastaco=s
POLIS model (Prastacos, 1986a,b; Caindec and Prastacos, 1995)
now used in actual planning practice.
2.4.2.2 The POLIS Model
Within the United States a combined land use-
transportation model built around a single mathematical
programming formulation has recently been applied within the
San Francisco Bay Area. This follows a long tradition of land
use-transportation modeling which began in the Bay Area in the
early 1960s with a Lowry-derived approach, leading to a system
of two interactively operating models known as the Base
Employment Model (BEMOD) and the Projective Land Use
Model (PLUM) (see Goldner, 1983, for a retrospective summary
of these early efforts). During the 1980s the Association of
Bay Area Governments (ABAG) again developed a modeling system
for the region. This model is known as the Projective
Optimization Land Use System, or POLIS. Both the
mathematical and algorithmic details of POLIS, as well as a
description of the model calibration efforts, are described by
Prastacos (1986a,b), and more recently by Caindec and Prastacos
(1995).
POLIS incorporates a number of the theoretical
developments introduced throughout Sect. 2 of this review. The
model can be stated as a single mathematical program which
seeks to maximize jointly the locational surplus associated
with multimodal travel to work, retail, and local service
sector travel, and, significantly andjointly, the agglomeration
benefits accruing to basic-sector employers (Prastacos, 1986a):
36
Click HERE for graphic.
37
The term )Hi refers to the number of new households locating in
zone i. Its inclusion in Eq. (21) is made clear below.
The joint objective function given in Eq. 21 incorporates
two spatial entropy terms, two travel cost terms (both for work
and service-sector trips, respectively), and a term which
adjusts the zonal distribution of basic employment within the
region. This is maximized subject to a significant number of
linear constraints. These include the usual non-negativity
constraints on all flow and stock variables as well as
constraints to ensure consistency between the flows (work
trips, dollars of retail and service expenditures) generated by
the model and the number of workers and households in each
zone. They also include a set of linear planning constraints
which both ensure consistency between the amount of residential
and industrial land available in each zone and the additional
amount of new housing and new employment assigned to those
zones by the model. Finally, zonal totals for households and
jobs are reconciled with county-wide sectoral as well as
spatial totals in a manner that reflects the spatial
agglomeration economies of basic sector activity at this
more macro-spatial level. For example, the allocation of new
households to zone i is subject to the following constraint:
Click HERE for graphic.
38
Click HERE for graphic.
Within POLIS this index represents the propensity of local
service sectors to locate near new population centers, using in
this instance the number of new houses built in zone i in the
prior period (t ! 1), )Hi,t-1, to reflect such opportunities.
The zonal agglomeration factors, fin, are of considerable
interest since they extend the approach beyond the basic Lowry
framework to provide a linkage between traditionally
accessibility-determined nonbasic activity and traditionally
exogenously determined (and incrementally projected) basic
economic activity. Despite extensive early recognition of the
importance of agglomeration economies in the growth of urban
systems, as Berechman and Small (1988) note, little has been
done to bring such effects into operational models.
Lacking any data below the county level against which to
construct such functions, they are estimated by factoring the
base year zonal employment totals in sector n, Ein, to be
consistent with both county-wide and regional employment totals
and recent rates of growth. These county and sector-specific
employment levels are themselves estimated as functions of
prior period employment levels in both basic and nonbasic
sectors. For example, for the manufacturing sector, n=1, this
equation has the general lagged, linear form
Click HERE for graphic.
39
where the changes in employment variables, (represented by
E,) denote changes in the previous time period.
As Prastacos (1986b) points out, however, using
equations such as (29) in longer-term forecasting may produce
erroneous results, since the coefficients should not remain
constant if the model is indeed expected to capture the shifts
in locational patterns. He proposes either the use of relaxed
versions of these regressions or derivation of confidence
intervals for each of the 2 parameters; a significant extra
modeling burden. We return to this topic of urban
agglomeration tendencies in Sect. 3 of the review.
Prastacos (1986b) describes the practical implementation
of this model for the Bay Area, including a discussion of data
sources and the multistep procedure required for model
calibration. The nine county San Francisco Bay Area, which
includes some 5.2 million residents, was divided into 107
planning and traffic zones. Two basic economic sectors
(manufacturing; transportation and finance, insurance and real
estate) were modeled, as were a single Aretail@ and a single
Aservices@ sector, using selected SIC codes. Employment in the
primary sectors of agriculture and mining are also allocated to
zones by POLIS, using base year conditions and land
availability to determine these natural-resource-constrained
activities.
Two transportation modes are modeled, termed private
and public. Calibration consists of choosing values for the
parameters $w, $sk, 8, and "n. This is accomplished by first
calibrating the spatial interaction submodels to obtain the
work and retail travel flows, Tijm and Sijk, by matching the
entropy levels in both model-generated work and service trip
matrices to "observed" data. In the case of the work trip
model this process also requires iteration with a logistic
modal split model, so that not only is 8 calibrated but it is
also used to weight the resulting work trip destination model's
bimodal (private and public) i-to-j cost matrix [recall Eq. (4)
above]. A single $s is calibrated toboth service and retail
sectors. Once suitable mode and spatial interaction model
parameters are found, a separate calibration stage uses these
best-guess values to search for suitable "n values which would
existing spatial (zonal) agglomeration of activities in the two
basic sectors. This calibration process is the reverse of
that used in most previous models, which typically
have begun with the calibration of the parameters affecting
activity location decisions, followed by calibration of the
travel behavior parameters.
POLIS represents an ambitious attempt to bring a range
of planning constraints as well as a concern for spatial
agglomeration economies into a practical land use modeling
process within the context of consumer surplus, utility, and
entropy maximization theory. The approach also demonstrates
the viability of using methodological advances in nonlinear
programming coupled with the application of a number of useful
numerical analysis routines. Caindec and Prastacos (1995)
describe the most recent empirical application of the latest
version of POLIS to the Bay Area, including a detailed
description of a slightly modified mathematical model and the
associated calibration
40
exercise. This technical report also overviews the use of
POLIS as one step in a four-tiered modeling process used by
ABAG. A detailed description of this process, as applied in
the Projections 92 project, is provided by Brady and McBride
(1992). The process consists of using ABAG's Regional
Economic-Demographic System (REDS), a dynamic input-output
(I-O) model (see Sect. 2.4.3 below) which estimates regional
population and employment totals in 38 different industrial
sectors to feed data to the County Employment Forecasting
System (CEFS) model (Caindec, 1994). CEFS in turn uses
multivariate regression and historical data to estimate job
growth for 32 industrial sectors within each of the nine Bay
Area counties. These growth trends are then used as inputs to
POLIS, which forecasts the distributions of future population,
housing, and employment among 114 Bay Area analysis zones.
Finally, these POLIS-generated forecasts are used within the
Subarea Projections Model (SAM) ( see Yang, 1993) to allocate
employment (by three Abasic@ and three Alocal serving@ SIC
categories), population, number of households, land use, and
forecast household income and its distributions across the
region's 1,209 census tracts. SAM then uses a series of
incremental formulas based on combinations of base year
activity levels and survey-based "Adevelopment potentials," the
latter defined in terms of acreage, housing units, and'
employment opportunities.
As described by Brady and McBride (1992), this four-tier
spatially hierarchical modeling process uses historical data
from 1980 and 1990 to generate inputs to a series of forecasts
for the years 1995, 2000, 2005, and 2010. The system has a
number of uses. It helps the region's planners address issues
associated with the allocation of federal and state funds to
not only transportation infrastructures but also sewage
treatment plants and other capital faciltities. The system's
projections are also used to inform mandated housing needs
studies for each city and county in the region, to inform local
government congestion mitigation plans, and to provide inputs
stimation of stationary and mobile- source air pollutants. The
system-s documentation provides considerable technical detail
and an excellent perspective on the role of integrated land
use-transportation planning models within the larger
urban/metropolitan planning process. It also educates the
reader as to the considerable data requirements and level of
effort required to generate such planning forecasts; a process
developed over many years in the Bay Area.
Currently missing from the framework is any form of
detailed, congestion-sensitive network routing submodel. On a
more conceptual note,an unresolved debate within the literature
concerns the use of optimization frameworks which seek to
jointly solve for both travel activity patterns and urban
activity allocations. Much of the issue revolves around
whether forcing a jointly optimal solution is a valid target
for simulation,
A probit model was calibrated against 1990 Census data to
project household income distributions within each census
tract.
41
given the general instability inherent in, and many additional
factors conditioning, urban growth and change. A subissue is
the extra computational time and more sophisticated
optimization routines it may take to achieve such a jointly
optimized solution. The conceptual issue is indeed a complex
one and needs to be tied to the specifics of each model's
underlying assumptions, computational form, and intended use.
There is no doubt that the above mathematical programming
developments have helped analysts to shed new light on the
meaning of different model structures. They have also provided
an effective mechanism for simultaneously introducing a variety
of planning constraints into the problem. As to whether, or to
which set of planning variables, we need to jointly optimize
over may depend on the question being asked. It should
certainly depend on the time frame being modeled. Complicating
the issue is the tendency to associate a model's objective
functions with particular, and in general partial, forms of
economic as well as spatial equilibrium; opening up a whole
theoretical debate involving the temporal progression in both
travel and nontravel prices and their resulting influences on
urban form. This issue is developed further inSect. 2.5 below.
2.4.3 Multisectoral Spatial Modeling Using Input-Output
Frameworks
2.4.3.1 Background
A third line of development draws its inspiration from the
intersectoral IBO approach to economic analysis introduced by
Leontief ( 1967). In particular, this approach provides a
general framework from which to begin to integrate the
manufacturing and other basic industrial activities, which are
treated as exogenous inputs to the urban development process by
Lowry- based models. The basis of this approach is to extend
the classical IBO model to include spatial disaggregations.
Notable early developments in this area include the work by
Leontief and Strout (1963) and, bringing entropy and therefore
logit forms of interaction model into the process, by Wilson
(1970, Ch. 3).
Click HERE for graphic.
42
Click HERE for graphic.
Click HERE for graphic.
which is an intersectoral, destination-constrained spatial
interaction model in the popular logit form. With similar forms
also possible for origin (production)as well as both production
and attraction-constrained coefficients, such developments
extend Lowry-like intersectoral modeling, in concept at least,
into more comprehensive basic and nonbasic frameworks.
Other variations on such intersectoral/interzonal
modeling are discussed in MacGill and Wilson (1979) and
Wilson et al. (1981), who also show how such models may be
embedded within a variety of entropy-maximizing, utility-
maximizing and spatial surplus-based mathematical
programming formulations.
2.4.3.2 The MEPLAN Model
A number of operational models make use of such
developments in intersectoral I-O modeling, including the
MEPLAN, TRANUS, and Kim models listed in Table 2.
MEPLAN appears to offer the most experience with, and
elaborate extensions of, the approach to date. The following
description is based on Hunt and Simmonds (1993) and Hunt
(1993, 1994).
Land and transport are treated in MEPLAN as two
parallel and interacting markets. Behavior in each system is
modeled as a response to price or price-like signals (including
travel disutility). As with other operational approaches a key
relationship is the effect on locational accessibility of
travel cost and time changes, which find their way back, in a
temporally lagged manner, into a set of activity-location
models. This once again occurs in a Lowry-like context, but
within a much extended set of sectoral selection options,
subject, given suitable data, to explicit market pricing
variables. Within MEPLAN, the demands for transport are
calculated directly from the interactions predicted by the
spatial economic system defined within the land use model. The
need for a trip distribution modeling step is obviated by the
direct translation of what are
43
termed trade flows, or "trades" from the land use model into
suitable modal volumes. An elaborate interface between the
land use and transportation models translates these trade
flows (labor, materials, services) into mode specific trip
matrices. Trips are assigned to modes by logit models and,
subsequently, onto the highway network using a version of
Dial's (1971) probabilistic, multipath assignment routine that
takes into account costs and congested travel times. In terms
of the simulated dynamic, land use is influenced by the pattern
of use in the prior period and by previous period transport
accessibilities. Transport is influenced by previous
infrastructure and present activity patterns arising from land
use.
Click HERE for graphic.
44
The transfer of factors between land use zones is
introduced by allowing demand arising in a given zone to be
satisfied by production brought from other zones using
thefollowing logit model:
Click HERE for graphic.
45
Click HERE for graphic.
Running the MEPLAN model involves solving
simultaneously for the above equations, in practice via a
sequence of nested iterations. Hunt (1993) describes the above
process as a series of Achains@ in prices and costs that run
opposite to the Achains@ in demand (the IBO structure),
beginning whenever a market price is determined by a constraint
on supply (typically supply of space) and resulting in the
simulated dynamic, land use is influenced by the pattern of
such use in the prior period and by previous period transport
accessibilities. Transport is influenced by previous i
nfrastructure and present activity patterns arising from land
use. Once the system of land prices and trades has settled
down to provide a single point in time representation,
recursion then moves the system from one equilibrated point to
anotherCa cross-section static-recursive system supplemented by
judicious use of lagged effects between some variables.
Within this general framework, a range of different travel
modes, household groups, and industrial sectors have been
tailored to specific studies (see Hunt and Simmonds, 1993, for
examples). This can include walk and mixed modal trips;
assignment of combined freight and passenger flows to networks;
the modeling of work, education, shopping, and other nonwork
trips, and home delivery of goods. A further MEPLAN module
uses the results from these models to carry out a detailed
cost- benefit analysis, including social and environmental
indicators. While data requirements for a fully implemented
model are potentially rather daunting, Hunt and Simmonds (1993)
claim that the generality of this highly synthetic modeling
framework allows it to be tailored to handle relatively modest
data inputsCno more than less comprehensive systems which do
not contain any land rent, production costs, or other pricing
variables. This is a testimony to many years of software and
model refinement. They do, however, point out the difficulties
often involved in selecting the model's many parameters,
typically involving extensive iterations and retrials, not
always in a purely automated fashion (see Hunt, 1994, for a
discussion of this process).
46
2.4.4 Contributions from Urban Economics
2.4.4.1 Background
The concept of treating both land and transportation
systems as market processes with endogenously determined
costs, as exemplified by MEPLAN, grew out of the urban
economics literature. Beginning with the work of Wingo (1961)
and Alonso (1964),this involves the application of neoclassical
economic theory to urban land use patterns, notably residential
land use,which is allocated across space on the basis of a land
market clearing process. Mills and Mills and Hamilton (1989)
and Bertuglia et al. (1987) provide reviews. Under this general
approach, individuals are assumed to maximize utility by
selecting an optimum residential location,which in turn depends
on a trade-off between housing price (which in the early models
simply decreased with distance from the CBD) and transport
costs (which increased with distance from the CBD). his trade-
off is represented in the form of a "bid-rent function," which
describes how much each household is willing to pay to live at
each location. On the supply side, each location is simply
assumed to be rented to the highest bidder. Such bid-rent
functions are now incorporated in a number of operational
models.
The work of Mills (1967, 1972), using linear
programming formulations, further advanced the notion of a
spatial market equilibration process in which stability occurs
when all households of a given type (typically reflecting
income group) are located so as to be equally well off.
Subsequently, these same notions have been extended by Anas
(1984) and Kim (1989) into more comprehensive, nonlinear,
entropy/utility- maximizing and network-based programming
forms. This includes empirical work to implement their ideas,
both using Chicago area data. This work also has important
overlaps with the combined modeling of Boyce and colleagues
(see Boyce et al., 1983, 1992, 1993) discussed in Sect. 2.4.2
above, where the ideas of systemwide optimization across a
number of choice dimensions (mode, location, etc.) find a basis
in the search for a suitable, systemwide equilibration of
various travel and land use supplies and demands. An excellent
text by Oppenheim (1995) now also offers a comprehensive
mathematical treatment of the connection between individual
choice behavior based on an economic (utility maximizing)
rationale and an urban system's behavior in searching for an
equilibration between transportation supplies and travel
demands.
2.4.4.2 Kim's Chicago Model
By combining Mills's ideas of a general urban system
equilibrium with Wilson=s approach to probabilistic spatial
interaction, Boyce et al.'s notions of combined transportation-
facility location models, and Beckman et al.'s concept of
equilibrated demand and supply over networks, Kim's Integrated
Urban Systems Model for Chicago (1989) offers a complex if
computationally tractable model with strong ties back to urban
economic principles. The model offers a general equilibrium
solution between the
47
demand for and supply of transportation and
activity locations in the strict economic sense. Like the
MEPLAN model discussed above it also determines prices
endogenously, if in a different way. It is selected for
presentation here because it shows quite clearly its strong
linkages to the type of inter-regional input-output modeling
described above, while also being formulated (and therefore
succinctly presentable) within a single mathematical
programming framework. Specifically, Kim's combined model of
"land use and density, shipment route and mode choice with
network congestion" has the form (Kim, 1989, p. 88):
Click HERE for graphic.
48
Click HERE for graphic.
49
Click HERE for graphic.
Substitution between land and other inputs is represented
by the aqrs coefficients, in which s represents a production
technology which equates with various intensities of land use.
Within the model, goods and services can therefore be produced
in tall buildings by using smaller land-output ratios and
higher capital- land ratios, as typically observed in the
service sector in many urban areas.
T he objective function (43-44) is a joint minimization of
the solution to a Wardrop equilibrium assignment of flows to
network links [recall Eq.(6)B( 9) above]; the total costs of
exporting commodities out of the urban system; and the total
land plus rental costs summed over all zones, commodities, and
production techniques used in the urban system. Equation (45)
ensures that the model-assigned link traffic volumes equal the
volumes assigned to all origin-to-destination specific paths
using that link, and Eq. (46) constrains zonal exports of each
commodity r to match given totals. Equation (47) ensures that
the total amount of commodity r produced in zone i plus any
brought into it from other zones is at least equal to the
amount of r sent to other zones, used in other sectors, and
exported from the zone. Equation (48) ensures that all
commodity r production summed over all s-intensity land uses
equals the total production of r and that flows of r from i to
j are correctly summed over all modes and network paths used
in the traffic assignment model. Equation (49) ensures that a
suitable level of entropy (spatial dispersion) in destinati
logits), and Eq. (50) ensures that the amount of land used to
produce commodity r in zone i at various intensities of use s
does not exceed the amount of land available for the purpose.
Finally, Eq. (51) sets nonnegativity constraints on all flows,
zonal product totals, and exports.
Solution of the program yields a combined network
demand-supply balance supported by an allocation of activity
levels to zones which ensures that the marginal cost of
producing r at location i plus the equilibrium unit shipment
cost from i to j by mode k should equal the marginal cost of
producing r at location j. Also at equilibrium, commodity r
in zone i will be produced at intensity level s as long as the
net benefit associated with doing so is at least equal to the
capital (R) plus land (L) costs of producing a unit of r in i
at that intensity level.
Kim (1989) has managed to calibrate a version of this
model, at a rather aggregate spatial (zonal) level, using
various and extensive data sources collected for the Chicago
region. To date this model does not appear to have been
applied in a policy study to which its output was a required
contribution. Nevertheless, the various calibration routines
exist, and in this sense the model is an operational one. The
approach
50
demonstrates the possibility of bringing important
aspects of urban economic theory into intersectoral, spatial-
interaction- based discrete choice models in order to move
towards more comprehensive urban modeling frameworks. As
described, the model does not contain a procedure for
translating its activity allocations into actual land use
arrangements within zones. However, it does operate directly
upon detailed representations of modal (highway and rail
transit) transportation networks. In the above form it appears
best suited to a decidedly strategic, multiyear analysis of
alternative urban development options.
2.4.5 Uses of Micro-Analytic Simulation
2.4.5.1 Background
Microanalytic simulation, or Amicrosimulation@ for short,
refers to the method of generating random numbers from within
prespecified probability distributions, which numbers are then
assigned to a specific response or response value. For our
purposes such a response may be associated with a particular
traveler attribute or with a specific travel choice. The idea
is to generate a series of traveler attributes and/or travel
choices in this manner, to build up a detailed representation
of specific trips or multitrip travel activity patterns.
Summing over all of these individually simulated travel
patterns provides aggregate values for planning studies.
With the advent of low-cost, high-powered computers, this
procedure has become an increasingly popular analysis tool.
In recent years, the technique has been applied within a
range of multistage decision-making models. These include the
use of the Recker et al. (1986) STARCHILD model to represent a
complex series of individual traveler-within-household
decision- making processes and of the Harvard Urban Development
Simulation (HUDS) (Kain and Apgar, 1985) and California
Urban Futures Model (CUFM) (Landis, 1994), both large-area
housing simulation models, the latter with a potential for
analyzing transportation improvements. Most recently, Barrett
(1994) describes an ongoing set of developments in which Monte
Carlo simulation once again plays a key role. This is the
TRANSIMS modeling effort funded by the Federal Highway
Administration as an experiment in simulating a complete
areawide set of individual traveler-based urban activity
patterns. Microsimulation is also the technique around which
the MASTER land use-transportation model described below is
constructed (Mackett, 1990b).
The principal utility of the microsimulation approach is
that it lets us incorporate a number of dimensions of both
individuals and their choice processes which would otherwise
require an excessive amount of disaggregation in model-based
accounts. Within the Dortmund model listed in Table 2, for
example, Monte Carlo based microsimulation is used to simulate
the intraregional migration of households as a search process
on the regional housing market (Wegener, 1982b). Here the
technique was used to overcome an otherwise impractical
disaggregation of this submodel into 30 household
51
types, 30 housing types, and 30 traffic zones, yielding 24.
million possible kinds of moves to be analyzed.
A second appealing feature of the method is that it is
relatively easy to understand and to implement. To create a
piece of software to simulate a particular process using Monte
Carlo simulation, all that is required is a suitable random n
umber generating routine, a suitable probability distribution
(or the raw data itself, perhaps in histogram form), a routine
for allocating values between 0.0 and 1.0 to randomly selected
choices on the basis of this distribution (data), and a routine
for collecting the results of the sampling exercise. All are
readily available today on personal computers. A more
significant challenge involves the acquisition of suitable
data, the determination of a suitably representative sample
size for analysis purposes (for which well established methods
exist in most cases), and the ability to place such sampled
responses within an appropriate modeling framework. Procedures
must also be developed to capture the cumulative effects of
common activities such as traffic congestion and spatial
agglomeration of commercial or industrial activity. This
raises some interesting and challenging questions for model
design, issues not yet clearly elaborated within the
literature.
A third useful feature of the method is that it allows not
only the explicit tracking of simulated individuals'
(travelers, households, companies) status over time, but also
a detailed tracking of the simulated changes in the use of
individual land lots. Where suitable time series data exists,
even at a quite aggregate level of resolution, this provides a
seful means of checking the reasonableness of the model
processes underlying the simulated outcomes. Scrutiny of such
microsimulated temporal paths has also been found by the
present author to provide useful insight into the implications
of using alternative model forms as well as alternative
parameter values to replicate a particular multistage process
(Dale et al., 1993). Certainly, microsimulation offers a good deal of
flexibility in experimenting with different event sequencing,
which is not present in traditional land use or transportation
planning models. Its application to the nature and timing of
landowner and land developer decision-making processes offers
an interesting possibility here.
2.4.5.2 The MASTER Model
The MASTER model (Micro-Analytical Simulation of
Transport, Employment and Residence), developed in the United
Kingdom by Mackett (1990a,b), is an integrated land use-
transportation model based on microsimulation, using Monte
Carlo methods to simulate the decision processes that a set of
individuals and their households go through over time. The
following description is based on Mackett (1990b), where
additional details are to be found.
52
Households are considered one at a time, but are grouped
together at certain points in the simulation to allow use of
aggregate values. The first processes considered are
demographic ones,including aging, giving birth, dying, divorce,
and marriage. Population change is modeled explicitly within
the model. Marriage and divorce lead to the creation of new
households. With divorce, what were once joint possessions,
including children and automobiles, are divided up. Divorcees
become one class of "forced movers." Voluntary movers include
newly married couples, singles leaving the parental home, and
wholly-moving households influenced by changes in a family's
life cycle. Both public and private housing markets are
recognized, and dwelling occupancies are tracked from one
period to the next. Choice of residence zone is based on a
weighted function of generalized travel to work costs for the
head of household. Both the supply of jobs and dwellings are
exogenous, zone-specific inputs to the model. Zone size appears
to be at the discretion of the modeler, recognizing of course
the geographic detail contained in the available data. Choice
of dwelling type is based on household size and composition.
Other household members'job selections are also considered, and
changes in economic status for simulated individuals may
include redundancy and retirement. The availability of vacant
dwellings is tracked for each residence zone in the system.
Changes in economic activity are considered after a household
moves residence. Young people become economically active as
a function of their education level, sex, and parents social
group. They become employed, unemployed, or, eventually,
retired. Retirements and job changes create job vacancies.
Jobs are associated with specific salary ranges.
The transportation processes modeled are becoming an
auto license holder, car ownership, car availability, and
choice of mode to work: each variously functions of age, sex,
household income, household composition, and mode-specific
costs of travel. To change mode of travel to work, either a
change in job or home location or a change in vehicle o
ownership or availability must occur or a significant change
in travel costs must be introduced. Logit forms are used to
select the mode of travel. If family members work along the
same travel corridor, carpooling is also possible. Only the
work trip is discussed, shopping and other trip purposes are
not included in the process described. Assignment of traffic to
specific routes is also not included in the model, therefore,
congestion is not modeled explicitly. Mackett suggests that
including such a routine would be relatively
straightforward. However, it would require an expansion of the
results from the 1% sample of households he suggests is
sufficient to calibrate the model, up to a 100% sample for the
purposes of placing the aggregate travel demands for roadspace
to network capacities. It=s not clear how this would be
accomplished.
Mackett (1990b) compared the application of the MASTER model
with the Leeds Integrated Land use Transportation (LILT) model,
a more traditional, if extensively modified, Lowry-type of
zonally aggregated simulation model. In his analysis, he
compared the sensitivity of the two models to large increases
in bus fares and automobile operating costs. He computed two
sets of model-specific linear elasticities for
53
automobile ownership; mode choice to work; and work-trip
length, time, and costs changes. He also compared linear
elasticities associated with employment and population
redistribution over the 20-year time frame. His general
finding was that the MASTER model produced sensible results,
and that differences in elasticities between the two models
were readily interpretable.
While such findings are reasonably encouraging, Bonsall
(1982) points out that microsimulation is not panacea for data-
hungry simulation models. He concludes that using the
technique in conjunction with suitable travel activity
scheduling models (see Sect. 3.3 below) and sample enumeration
techniques offers some attractive possibilities. However, he
emphasizes the need to establish carefully the accuracy of the
mechanisms being simulated and, in particular, the
applicability of generic procedures to different traveler
groups.
2.5 APPROACHES TO URBAN DYNAMICS
2.5.1 Background
Figure 6 presents a general representation of the sort of
discrete multiperiod dynamic employed by currently operational
systems in their attempts to simulate the evolution of the
urban system. The usual means of forecasting the effects of
different transportation system improvements into the future
is to "fit" both the transportation and land use models to a
base year, denoted as time period t, and then try to project
these same relationships forward into time period t + 1. The
time interval between time t and t + 1 in Fig. 6 varies by modeling
system, from as little as 1 year to as many as 30. In the
simplest case, a single 20-or 30-year time interval may be used
to project both transportation and land use forward in time
under various investment and growth scenarios (the Puget Sound
Council of Government model used such an approach). A better
approach is to iterate through successive shorter-term(1-, 2-,
or 5-year) forecasts, using the results from the latest
forecast as a baseline for each subsequent projection. Some
model structures can allow both options. Anas (1994) indicates
that the METROSIM modelcan be used either to obtain a one-
shot, long run equilibrium forecast for transportation and land
use in a metropolitan area, or to create a sequence of annual
changes in both land use and transportation which can be run
until convergence to a steady state is achieved.
Difficult to predict changes, such as changes in the
location of new basic employment, are usually handled, even
within the more advanced models, in an incremental fashion and
often treated as exogenous inputs. Residential, service, retail,
and, in some cases, selected manufacturing employment
activities are then advanced and redistributed on the basis of
travel cost-adjusted locational accessibilities. How this occurs in
practice varies by modeling system. Both residential and
employment activity
54
Click HERE for graphic.
55
locations may be stabilized within a single time period, or one
may be related to another in a subsequent time period using
lagged equations (recall the ITLUP model description above).
The accessibility-based travel patterns which result from such
redistributions are often simulated to reach a stable demand-
supply equilibrium during the current time period.
Alternatively, the process may become a more open-ended one, in
which constant readjustments in both land/floorspace
allocations and transportation infrastructure and services are
taking place within lagged equation forms (see Wegener, 1994,
for a discussion). For example, Hunt and Simmonds (1993)
conceptualize the urban dynamic simulated in MEPLAN as follows
(pp. 223-224): "In each market there is at any time an
adjustment towards equilibrium. However, this adjustment is
limited. It is limited by 'the impossibility of instantaneous
changes in either building stock or transportation
infrastructure and by the imperfection of the information
exchanges in the system. This leads to delays and lags in the
adjustment of the system to its own price and congestion
signals. The result is that the urban structure continually
moves toward but probably never reaches an equilibrium."
The implication is that transportation system changes,
notably major infrastructure investments in new highways or
rail transit lines, will need time to affect urban land use
patterns. Once introduced, such land use patterns may then
also, but within shorter time frames, induce further changes in
urban travel demand. Just how this is accomplished in terms of
intraperiod versus interperiod increments often depends upon
the time interval chosen between model iterations, which in
turn usually depends upon the original purpose behind a model's
development.
Greater subtlety as well as realism is introduced into
the more elaborate modeling approaches by allowing different
rates of change in housing and transportation stock
adjustments versus residential and employment activity
reallocations, or versus short range travel (mobility)
adjustments. Wegener's (1986) model for Dortmund provides one
of the most conceptually satisfying implementation of such
ideas in practice. His approach is presented briefly below.
2.5.2 The Dortmund Model
As we learn, and perhaps in order to learn, more about the
true nature of urban system dynamics, it appears that
increasingly comprehensive urban simulation models are
required. The Dortmund modeling system, along with MEPLAN,
METROSIM, and the 'Bay Area system of models containing POLIS,
all strongly reflect this trend. Wegener's Dortmund model is
selected for review below. It not only offers one of the most
advanced implementations of a multistaged urban land use-
transportation systems dynamic to date, but also makes
innovative use of spatial interaction models as well as
microsimulation methods within its framework.
56
The Dortmund modeling system was developed for the
city of that name in Germany by Wegener and colleagues
(Wegener, 1982a,b; Wegener, 1986; Wegener et al., 1991).
Dortmund, as discussed below, refers to the intermediate level
model in a three model hierarchy. Within this hierarchy, a
macroanalytic model of economic and demographic change
simulates employment by industrial sector and population by
age, sex, and nationality within each of 34 labor market
regions, as well as interregional migration rates within the
State of Nordrhein-Westphalen. Dortmund is a mesoscopic
spatial model which uses this regional context to simulate the
intraregional location decisions of industry, residential
developers and households, and associated public policy impacts
in the fields of housing and infrastructure. The model was
developed primarily to study the impacts of long-range economic
and technological change. The model was also used recently by
Wegener (1995a) to examine the effects of urban activity
reorganization on the reduction of carbon dioxide emissions.
The Dortmund model is applied to a 30-zone region
centered on the city of Dortmund, a region with a population of
some 2.4 million residents. At the third level in the complete
model hierarchy is a microanalytic model of land use
development within any subset of 171 statistical tracts in the
Dortmund urban region. Tracts vary greatly in size, but the
majority contain between 2000 and 5000 residents (Wegener,
1982a). The purpose of this more spatially detailed model is to
allocate construction generated by the mesoscopic or zonal
Dortmund model to tracts within a zone.
The following description is focused on the mesoscopic
model only and is based largely on the version described in
Wegener (1986). A simulation run involves seven interlinked
submodels dealing respectively with (1) car ownership and
transport; (2) aging of people, households, dwellings, and
workplaces;(3) relocation of firms, redundancies, and new jobs;
(4) nonresidential construction and demolition; (5) residential
construction, rehabilitation, and demolition;(6) labor mobility
(change of job); and (7) household mobility (change of
residence).
A good deal of thought has been put into the issue of
simulating urban dynamics. Wegener (1986; see also Wegener,
Gnad, and Vannhahme, 1986) classifies urban and regional
changes as falling within either fast-, medium-or slow-response
processes. While relatively rapid changes in mobility can be
brought about by trip mode or route choice and, possibly a
little more slowly, by home, job, or firm relocations, much
slower processes are involved in changing the more expensive
physical structures of the city (its housing, factories, office
and shopping centers, and transportation routes). Also at work
are medium-speed changes, involving either socioeconomic or
technological developments forced on the area by broader
regional or national influences: such as economic cycles,
biological changes such as population aging, or the advent of
new technologies which are again not area controlled but over
time are area affecting.
57
This conceptual framework is translated into practice in a
number of ways. Rather than simultaneously determining
locations as trip ends in a unified transport-and-location
equilibrium, an explicit separation of the transportation and
land use subsystems is maintained. The transportation model
iteratively solves for a user-optimal set of flows where car-
ownership rates, trip rates, trip destinations, and mode and
route choices are in capacity (congestion) constrained
equilibrium; accomplished by using an extended version of
Evans'(1976) algorithm. At the trip distribution stage this
involves calculating sixteen interrelated spatial interaction
models for work, shopping, services or social, and education
trips for four socioeconomic groups and three travel modes:
car, public transport, and walking. First, however, household
car ownership and trip generation rates are computed within an
iterative process which makes such choices nominally subject
to a household budget constraint on travel and car ownership
costs. Within this framework, mode choice is nested within
destination choice and recognizes car availability as well as
generalized travel costs (recall the discussion in Sect. 2.3
above).
The framework distinguishes in a reasonably traditional
way between nondiscretionary forms of travel (work and school
trips) and discretionary travel, such as shopping and social
trips. It does this by using doubly-constrained spatial
interaction models for the former and production-constrained
forms (logits) for the latter. However, the work trip model is
solved only once, for the base year. Subsequent and matrix
element specific adjustments to this homeBwork trip matrix then
rely on direct inputs from the submodels dealing with change in
residence and change in job respectively. This is done to get
around theproblem (also noted by Mackett, and by Wilson) of
inappropriately using doubly constrained spatial interaction
models in a dynamic context. That is, such a model may require
that workers who have changed neither home nor workplace over
the current time period be assigned to another cell in the work
trip matrix in order to satisfy a revised set of zonally
aggregated trip generations (workers) and attractions (jobs).
This leads to an overestimation of the effects of changes in
transport costs on the resulting pattern of urban commuting.
By limiting the ability of such interaction models to
reallocate work trips, the impacts of transportation costs on
the subsequent location of these home and workplace activities
becomes less direct, more lagged and more aggregate in its
effects than would be the case if spatial interaction models
were used less discriminately. The reader is referred to
Wegener (1986) for an elaboration of this matrix-adjustment
process.
Within Dortmund transportation cost and related
accessibility changes are anyway not the only determinants of
locational change. They are traded off against other non-
transportation variables which appear to be at least as
important in the evolution of urban form, a point made
frequently in the recent empirical literature (Giuliano, 1989).
The simulation takes place in 2-year cycles (up to a 30-year
planning horizon), allowing a ":perception delay" of 1 year, on
average, to take effect. The transport model is processed at
both the beginning and end of each 2-year simulation period.
Through the implicit lag structure of this recursive system,
changes in land use variables only become visible to
58
the transportation model at the beginning of the next (2-year)
time period. Longer delays are accounted for in some submodels.
New housing only finds its way onto the market three or more
simulation periods (6 years) after a simulated change in the
transportation system has occurred. As a practical matter, the
spatial distribution of urban activities is allowed to change
within the modeling process in two ways. One way is through
Aaging ,@ which in the model depends only on time and not
endogenously modeled choices. The mechanics of this aging
process involve the use of a probabilistic Markov process,
which is applied once each model iteration within an aging
submodel. An additional practical facet of the approach is the
recognition that the opening or closing of large industrial
plants may not be predictable by any modeling system, hence
their treatment as exogenously entered "historical events."
All other changes depend on accessibility based spatial choices
generated explicitly within the model. For this purpose the
model uses nested logits and a variant on the inclusive value
method described by Fiskand Boyce (1984) as its basic building
blocks.
3. USING INTEGRATED MODELS IN POLICY ANALYSIS:
AN ASSESSMENT
3.1 INTRODUCTION
Recognizing an inevitable lag between latest theory and
best practice, if we were to evaluate currently operational
models on the basis of their collective ability to incorporate
latest theories within their frameworks, they would get quite
high marks. Taken as a set, the previously reviewed integrated
models have advanced in a number of important directions since
the 1960s. They have managed to combine the minimum effort and
locational accessibility premises inherent in spatial
interaction theory with the statistical and information
theoretic notions of entropy and the economically rational
notions of utility maximization. Methodologically, they make
use of nonlinear mathematical programming methods as well as
the latest developments in econometric and microsimulation
modeling of the demand for travel, residences, and employment.
The more comprehensive models also tackle demographic change in
the urban population, and some also model physical stocks other
than transportation infrastructure, notably the aging and
renewal process associated with the urban housing market.
Finally, they model these events using an extensive database,
resulting in the allocation of traffic volumes and speeds over
detailed link-node representations of multi-modal urban
transportation networks.
But this, of course, is not the test in which we are most
interested. How well such theories stand up in practice is the
true test. Here we are currently at something of an impasse.
In contrast to the considerable effort made to develop the
theoretical aspects of the relationships between transportation
and spatial structure, the practical application of models has
been relatively neglected. This conclusion is mirrored, with
respect to U.S. practice, in the review by Cambridge
Systematics and Hague Consulting (1991), which found that only
a handful of the top 18 metropolitan areas were using
integrated models in their planning processes. In their
relatively brief history, the land use-transportation models
reviewed in Sect. 2 have been subjected to a good deal of
criticism (see Batty, 1980, for an early historical review;
see also the Winter, 1994 edition of the Journal of the
American Planning Association for a retrospective). Past
criticisms have tended to revolve around (1) conceptual issues
of model realism and hence usefulness; (2) practical issues of
data availability and quality, as well as computational
requirements and ease of use; and, as something of an offshoot
from these two issues, (3) the role such models are to play in
the planning process. While recent computational advances have
done much to remove concerns over both computer costs and
computer run times, the other issues remain. Each is discussed
below, highlighting some concerns frequently voiced in the
recent literature.
59
3.2 MODEL VALIDATION ISSUES
As Lee (1994) points out, the role of large scale urban
land use-transportation simulation models remains a cause for
debate. Should they be considered as tactical or as strategic
planning tools? If used as tactical planning tools, their most
common application would probably be to evaluate travel
policies along specific urban corridors, with an eye to an
environmentally influenced benefit-cost ratio being realized
within a suitable time period. Even so, such an evaluation
period might cover as long as 15 or 20 years depending on the
TCM being proposed (i.e., up to the expected lifetime of a
typical urban highway pavement, if the addition of new
infrastructure is involved).
However, a danger with using models solely to analyze
individual travel reduction projects is the potential for
disjointed, piecemeal planning. Ideally, and central to the
aims of this present review, we need to find a way to embed
such project evaluations within more strategically developed,
areawide transportation plans. If these plans are to make the
sort of contributions to petroleum savings and CO2 reductions
which have come from more efficient engine and fuel
technologies, areawide impacts will almost certainly be
required. We also need to think in terms of longer planning
horizons. Watterson (1993) concludes that even a planning
horizon of 30 years may not be long enough to capture the true
impacts of a plan which contains significant transportation
infrastructure investments. He notes that such plans may go on
to influence urban form, and therefore urban travel activity,
for many years into the future.
Lee (1994) argues that in searching for such a strategic
role we may be trying to get too much detail into our models.
As we add more detail and functionality to what are already
rather ambitious models, we loose flexibility in their
application and increase expensive data requirements. In
contrast, Harris (1994) prefers to view such efforts as an aid
to comprehensiveness of understanding, rather than
comprehensiveness in forecasting. This second argument meshes
well with Wilson's (1984) perspective on the use of integrated
models as tools for evaluating the robustness and resilience,
rather than the details, of alternative urban and regional
plans. As Owens (1989, p. 233) puts it; "In the end, perhaps,
accurate prediction matters less than flexible normative
planning, based on an intelligent assessment of the most likely
directions of certain trends."
To carry out such planning, mathematical, computer-
based models would seem to be our only realistic alternative if
we wish to apply, and properly test the results of applying, a
formally developed logic behind our planning decisions.
Without reasonably comprehensive models, we cannot hope to
simulate the often nonintuitive effects of combining a wide
range of policy options within any single plan. Our ability
to determine the general magnitude and direction of policy-
generated effects seems well worth the effort. This, however,
raises the issue of how we gain confidence in our model-based
results. Such a question moves us on to issues of model
validation.
61
Validation means carrying out checks to establish how
well a model did in forecasting a future situation by comparing
the model's results with observed data. As Wegener (1994, p.
25) points out, "remarkably few validation exercises are
reported in the modeling literature." Travel data
availability constitutes the major constraint on validation
exercises to date, especially data covering time intervals
long enough to capture some of the important changes in urban
infrastructure and land use.
An obvious problem for cross-sectionally calibrated
models is that they are using the many parameters established
in their base year calibrations to predict changes over time.
In doing so, they may be placing an overreliance on the
behavioral implications of spatial variability in traveler and
land owner responses to differing conditions. Of greater
interest is the temporal variability in such responses for a
suitable range of geographically as well as socioeconomically
varying urban environments. To understand and model such
behavioral responses, we need to make more and better use of
time series data. Webster et al. (1988) briefly describe the
results of using seven of the nine models covered by the
international study group on land use-transportation
interaction (ISGLUTI) to project both zonal employment and
population totals, using data for intervals from 3 to 12 years
into the future. This includes versions of MEPLAN used in the
studies of Sao Paolo, Brazil, and Bilbao, Spain, which
apparently used specially developed follow-up survey data for
the purpose (Echenique, 1985). All results reported R2 values
> 0.95 when comparing absolute values, using from 30 to 148
zonal observations, depending on
the particular model and its application. The 3-time period,
12-year, incremental forecasts produced by the Dortmund model
gave particularly high R2 values. However, R2 values took on
much wider ranges, from 0.98 to 0.59,when comparing observed
versus modeled rates of change in these same variables.
Similar R2 values are reported by Prastacos (1986b),
using the POLIS model to predict changes in the number of
households as well as employees per zone within two basic and
two nonbasic sectors for the period from 1975 to 1980. This
involved regressions on 107 observations (i.e., land use zones)
within the nine county San Francisco Bay Area. Noticeable
improvements in these coefficients occurred when aggregating
the results to county totals or when using such county totals
to control the subsequent allocation of employment to zones
within a county. Some checks were also made on the resulting
interzonal private and public trip matrices produced by POLIS,
but with synthetic rather than observed 1980 flows for
comparison. Recent recalibrations of the trip distribution and
modal choice (auto versus transit) submodels using 1990
journey-to-work data from the Census Transportation Planning
Package (Caindec and Prastacos, 1995) produced R2s for auto
trips around 0.80 and for transit around 0.77, with model
averaged travel times within 10% of expected results. In
general, however, producing similar comparisons of modeled
versus observed non-work trip matrices is problematic, with
little in the way of consistent historical data for guidance.
62
Hunt (1994) also describes an extensive series of model
validation tests carried out as part of the application of
MEPLAN to the city of Naples, Italy. Maps and graphs are used
to show the generally good fit between the model generated
versus observed number of households as well as the private
residential floorspace rents per zone (26 zones). Also
examined were (1) expenditures on travel; (2) floorspace and
other purchases by each of five household types; (3) average
trip distances for four trip purposes (work, shopping, school,
other); and (5) selected modeled versus observed average
weekday morning peak period traffic cordon counts. He also
describes the considerable time and effort required to
calibrate, or Afit@ the model, including the definition of
suitable household classes and the too often experienced
problem of having land use data for one year and
transportation systems data for another, somewhat earlier or
later one.
Whether using data sets from two or more periods to
forecast or "backcast", using a cross-sectionally calibrated
set of model parameters, ideal requirements for such tests
would include use of the same set of traffic analysis zones as
well as the same trip purpose definitions from one period to
the next. In the past this has often meant considerable data
reconciliation efforts. Wegener (1994) suggests that a
model's performance should bebased on its ability to forecast
the essential system dynamics over a past period at least as
long as the forecasting period to which it is being applied.
He goes on to note that only Dortmund and MEPLAN, among
currently operational models, appear to have followed this
philosophy. In both cases these models are only partially
calibrated by statistical estimation techniques and partially
by manual fine-tuning as part of a long, interactive
process. Often, as Hunt (1994) points out, it's difficult to
distinguish data problems from errors in a model's formulation
or in its underlying assumptions.
Clearly, greater emphasis on validating the models is
required, including the establishment of procedures to track
the major data sources necessary to calibrate them. This
constitutes the most significant obstacle to model validation
and, by implication, further useful model development. More
comprehensive models mean more demanding data requirements.
Given current data limitations, how are we to assess the
value of such models in a strategic context? Here the ideas
expressed by Cowing and McFadden (1984) and restated by
Hensher et al. (1992) are apropos. When an analysis task
involves forecasting over a long period of time with
substantial deviation from historical experience to be
expected, they suggest that assessment of a simulation model
is best focused on realism in process. This contrasts with
more direct assessment of a model's predictive capability,
involving the above discussed comparison of model results
against a known, and empirically observed, reality; a
validation process they term realism in performance. At the
present time any discussions of current model weaknesses and
associated research needs are necessarily focused heavily on
such realism in process. However, more realism
63
in process suggests that we also use more behaviorally
based (i.e., more realistic) models. That is, it suggests that
we focus more attention on how travelers behave and, for the
purposes of policy impact assessments, how such behavior
changes over time once policies are implemented which act upon
it. This in turn suggests that more attention be given to the
collection and use of longitudinal data sets. In particular,
multiwave traveler panel surveys, collecting information from
the same group of travelers at discrete time intervals, are
discussed below as an important data collection option. A
concerted effort will be required to design, collect, and
maintain such temporally anchored databases. A first step is
to determine which are the major variables of interest to such
longitudinal analyses and (since cost of data collection
remains the major constraint) which data we can effectively
relegate to less regular data collection activities. To do
so, we need to better understand the causes of current
variability in travel demand.
3.3 THEORETICAL ISSUES: TOWARDS MORE
REALISTIC MODELS
3.3.1 Household Travel Mobility Modeling
3.3.1.1 Criticisms of The Traditional Transportation
Planning Model
The traditional four-step transportation planning model
described in Sect. 2.3 of this review (Fig. 3) has been the
focus of a good deal of criticism for many years. Within the
United States, the need for metropolitan planning organizations
to address the vehicle travel reduction requirements of the
1990 Clean Air Act Amendments (CAAA) is now leading to a new
round of model development, known as the Transportation
Model Improvement Program (see Texas Transportation
Institute, 1993). Much of the criticism within the modeling
literature argues that we need to place both household and
company-based travel decisions within more behaviorally
realistic decision-making frameworks. Treatment of travel as a
good composed of separately modeled attributes of frequency,
mode, destination, and route choices is being challenged.
While energy, economic, and environmental impact analyses may
require that we translate the demands for travel into numbers
of temporally and spatially explicit vehicular trip volumes,
the current methods we use for getting there are proving
increasingly restrictive. Frequently voiced criticisms of the
traditional Urban Transportation Planning (UTPS) process are
described on the following pages. Cumulatively, these
weaknesses act to obscure the relationship between cause
(including policy-induced cause) and effect.
The Relationship Between Trip Frequencies and Travel Costs
An appropriate feedback mechanism between the trip
generation model and the rest of the four-step urban
transportation modeling procedure continues to elude modelers.
The dashed arrow in Fig. 3 shows the desired (hypothesized)
linkage. While travel speeds and costs are often
interactively solved for within the destination, mode, and
64
route choice steps, traffic generation remains inelastic with
respect to such travel cost changes. To date, no sound and
generally reproducible basis has been found for such a
linkage. Similarly, empirical efforts at direct incorporation
of the effects of cost-determined locational and modal
accessibility within existing trip generation models
have met with almost universally poor results (see Kitamura,
1994, for a recent discussion).
It may be the case that where trip frequencies are
concerned, even among the more discretionary forms of travel,
transportation costs or traditional forms of cost-based
accessibility are in many cases not the only, or even the most
important, determinants of daily or weekly travel activity
schedules. However, one difficulty associated with obtaining a
relatively simple functional relationship between trip
frequency and trip length or cost may be the nature of past
survey data. Cross-sectional, single day trip sampling may not
contain the information required to fathom a behaviorally
sensible and statistically consistent relationship. Implicit
in nearly all past efforts to simulate urban travel activity
patterns is the treatment of transportation as a separable
good to be purchased independently of other household needs.
However, once we place our analysis within a longer-term
perspective, other nontravel cost factors become important.
That is, housing, food, health, education, and other costs may
compete with travel costs for the household budget in ways
which may affect trip frequencies every bit as much as urban
accessibility surfaces do.
An argument for the use of integrated land use-
transportation models is that they currently offer the only
means of getting the costs of travel back into the trip
generation process; albeit via a rather complex series of
modeling processes. However, while a residential allocation
submodel is used to link housing rents to travel costs within
a number of the models reviewed in Sect. 2, these models don't
go any deeper into the trade-offs between travel and other
goods which take place within budget-constrained households.
For strategic planning purposes it may be sufficient to model
travel versus housing costs in this manner, as long as a
household's share of its income spent on travel and activities
remains reasonably constant (see below). However, many short
term decisions by household members may reflect a wide range of
responses to daily or weekly time as well as monetary travel
budgeting. The cumulative variability in such responses may
be an important reason why no simple empirical relationships
between daily trip frequencies and travel accessibilities or
costs appear to repeat themselves across different studies.
Trip Chaining and Destination Choice
Making the relationship between trip frequency and travel
cost more difficult to assess, many trip destinations in urban
areas occur within multipurpose, multistop daily travel chains
such as the home to work to shop to home type of travel circuit
(see, for example, the travel data described by Hummon and
Burns, 1981; Kitamura and Kermanshah, 1983; O=Kelly and
Miller, 1984). By ignoring such trip chaining activity,
65
the traditional transportation planning model fails to capture
the time and cost savings offered by multistop travel activity
patterns. This in turn means that integrated models of land
use and transportation which use traditional, single
destination spatial interaction models also fail to provide
support for the analysis of land use policies which might take
advantage of such mileage saving options.
The destination choice set problem, a frequently revisited
technical problem within the travel demand literature, further
exacerbates the problem of destination choice. Spatial
interaction models, whether calibrated at the zonal level or
fitted to a sample of individual traveler responses, require a
prespecified set of alternative destinations to choose from.
Removal of a possible destination from the available choice set
within a logit model changes the absolute probabilities of
selecting each of the remaining options. The behavioral
dilemma results from not knowing what the choice set really is
or how it differs across individual travelers at different
originating locations and by different trip purposes. While a
number of approaches to the problem have been tried (Stopher
and Meyburg, 1976; Richardson, 1982; Recker et al., 1986), the
usual approach is to allow all traffic zones in the system or
all zones chosen by survey respondents (where such data is
available) to be in the choice set. This approach recognizes that more distant and
less attractive locations will receive few or no trips and,
hopefully therefore, will affect the results only marginally
and well within the bounds of modeling error. Significantly,
the way in which multidestination trip chaining opportunities
affect such choice sets has not been thoroughly researched to
date (but see Recker et al., 1983 for some interesting work on
simulating feasible, including multistop, activity programs
for specific household members).
Discretionary and Off-Peak Travel Activity Modeling
Both time of day and time within the week need to be
recognized and modeled as important travel options. Nonwork,
and frequently non-peak, trips are now responsible for 78% of
annual trip starts and 73% of total vehicle miles traveled
(VMT) in the United States (Hu and Young, 1994). Also, since
many daily trip chains combine peak period commutes with more
"discretionary" forms of off-peak travel (e.g., shopping,
social and recreation, and personal business trips), this
needs to be recognized in some fashion if we wish to
understand the effects of such transportation control measures
as staggered work hours and compressed work weeks, or the
potential for mixed use urban activity centers to encourage
midday walk, paratransit, or public transit use for personal
business and shopping.
3.3.1.2 Implications for Modeling Travel Reduction
Strategies
Collectively, the above weaknesses render policy analysis
for specific highway impact projects rather suspect. For
example, the potential for a particular highway capacity
expansion project to lead initially to less congested, less
polluting travel may in
66
reality erode over time as the greater ease of travel over
this highway induces some travelers to change their daily or
weekly movement patterns, resulting in a revised form of
multidestination trip chaining activity and shifts in the
temporal distribution of traffic. Among other effects, such'
temporal adjustments may affect existing vehicle availability
within multidriver households. For example, the
introduction of an high-occupancy vehicle (HOV) lane as part
of a highway capacity expansion may induce ride-sharing, which
in turn leads to a rearrangement of vehicle utilization within
the household. Vehicles left at home by the ride sharing
commuter may then be used by a spouse or other family member.
In total, some of the new weekly household travel activity
patterns which form may, on balance, involve more VMT, consume
more fuel, and put more pollutants into the atmosphere than
before.
It's also not obvious how to introduce the effects of
emerging telecommunications options, such as telework or
teleshopping, into such a rigid trip-oriented approach. Are
such options considered trip generation or travel mode
options, and more importantly, how does the adoption of
frequent telecommuting affect the travel activity patterns of
other household members? The recent reports by DOT (1993)
and DOE (Greene et al., 1994) discuss this topic in some
depth, and recognize our currently limited understanding of
what to expect. Indeed, the whole area of in-vehicle as well
as in-home real-time information systems and their effects on
travel patterns raises questions not well suited to a single
destination, separable trip purpose approach.
Single destination trip based models also run into
problems in evaluating such low energy, and potentially low
emissions options as electric or hybrid fuelled vehicles.
Should petroleum prices rise sharply in the future, it would
help to know what percentage of household travel could be
supported by a single daily vehicle charge, given a particular
land use arrangement within which trip chaining is an option.
3.3.1.3 Some Recent Developments in Travel Demand
Modeling
If we are to make current models more behavioral, or
replace them with new models, the following areas warrant
further study and possible unification:
Analysis of Multi-Day Household Travel Activity Schedules
An increasingly popular approach to travel demand
modeling is to look for ways to link travel decisions more
closely to such lifestyle factors as intrafamily obligations,
leading to jointly organized trips. Such considerations place
us firmly within what is termed the "travel activity analysis"
literature (see Carpenter and Jones, 1983, and Kitamura, 1988,
for extensive literature coverage). This empirical and
modeling literature suggests that shifts in travel behavior
may only be properly understood within the wider context of
how people organize their lives over a series of planning
horizons
67
and, notably, over multiday rather than single day
periods (see, for example, Hirsh, Prashkea and Ben-Akiva,
1986; Kitamura and Van Der Hoorn 1987; Pas, 1988).
An important aspect of such an approach is the study of
how households make use of their various automobiles (and,
increasingly in the United States, of their light trucks and
minivans) to carry out their activity schedules. Hensher et
al. (1992) provide a review of past literature on this topic,
as a prelude to describing an econometric modeling approach
andsupporting empirical study of the various dimensions of
household-based automobile demand. In the United States,
this recent literature on vehicle utilization includes the
nested logit modeling of vehicle class, vintage, and fleet
size by Train (1986) and the ordered probit modeling of
household vehicle ownership decisions by Golob (1990). It
also includes the analysis of gasoline price effects on
vehicle use in multivehicle households by Greene and Hu (1984)
and the statistical modeling of multiday vehicle utilization
levels by Greene (1985). Greene (1985, p. 350B351) pointed
out that "in particular, while single-day surveys of a large
sample of households have been extensively studied and
modeled, there is a lack of information and analysis of how
usage of a particular vehicle varies from day to day over a
long period of time."
With a limited but growing collection of multiday travel
surveys for the past decade, notably in the form of panel
surveys (see below), this situation is beginning to change.
Much greater use of longitudinal data on travel activity
patterns, including vehicle utilization patterns, is needed if
we are to understand how policies intended to discourage low
occupancy vehicle travel actually affect behavior. Currently,
as Bhat and Koppleman (1993) point out, even conceptualizing
the activity scheduling framework within which travel and
other weekly activity decisions are made constitutes a new and
challenging task.
Microsimulation appears to be a natural candidate for
making operational such ideas as the simulation of multiday
household travel activity patterns, and efforts such as the
STARCHILD model (Recker et al., 1986) and TRANSIMS
(Morrison and Loose, 1994; Shunk, 1994) fall into this
category. Such efforts have a growing, if rather varied
theoretical andempirical literature on the modeling of
multistop travel chains to begin from, as evidenced by the
review of trip chaining research by Thill and Thomas (1987).
This literature includes the empirical work on vehicle use by
Hummon and Burns (1981); the empirical and theoretical work
with logit-based models of destination choice by Kitamura
(1984); the suggestions for using nested logits to identify
and capture the empirical linkages between primary versus
secondary destinations within such trip chains by Wilson et a
l., (1981); the use of microsimulation linked to logit demand
functions to investigate the effects of chaining on
locational accessibilities (Southworth, 1985b); the extensive
empirical and theoretical work on multistop, multipurpose
shopping trips by, among others, Narula, Harwitz and Lentnek
(1983) and O=Kelly and Miller (1984); and the use of Markov
models to assess the effects of trip chaining on the location
specific demands for retail facilities (O=Kelly, 1983). However,
these and similar ideas have yet
68
to find their way into actual use within the integrated urban
modeling systems described in Sect. 2 of this review.
Multi-Wave Panel Analysis of Household Travel Behavior
A new development in recent years has been the
emergence of travel panel analysis. By surveying the same
group of travelers or households at two or more intervals in
time, with successive surveys separated by a few months or
years, we are now beginning to acquire data on how travelers
actually responded to a specific event. Hensher and Raimond
(1992) provide a summary of the major transportation panels
studies to date. These include panels used to analyze
household vehicle ownership and utilization decisions, and the
effects of staggered work hours, an HOV lane, and
telecommuting on household travel behavior. Greater use of
such panels should allow us to get away from the always
suspect use of disaggregate travel demand models whose
calibrations are based on limited size, single cross-sectional
samples of urban residents.
However, travel panel analysis is still in its early stages.
The first text devoted exclusively to the topic was compiled
only recently (Golob, Kitamura, and Long, 1994). The recent
theoretical work of Jiang et al. (1992) and the related
methodological and empirical work by Hensher and Raimond
(1992) are particularly interesting. By treating the speed at
which potential travelers change their travel behavior as a
process of adaptation, Hensher and Raimond embed a stochastic
process within their differential equations model. In this
way they can affect the timing (including instantaneous
adoption) at which such changes occur from one state to
another (in their case a change in route from a free to a new
toll road). Here the considerable literature on hazard
response and survival models becomes very useful and is likely
to be visited more often in future travel-related research.
These last authors also describe the problems involved in
translating data from a series of discrete time panels into a
continuous time stochastic model of the real world process.
Currently, we are at a relatively early stage in the design
and use of such models and also in our design of the panel
data sets which can best support them. In one of the best-
documented research efforts to date, Hensher et al. (1992)
collected four waves of panel data, spanning a 70-month
period, from households in metropolitan Sydney, Australia.
They use this data to develop an automobile market share and
energy demand forecasting system based on a combined discrete-
continuous econometric model of household automobile choice.
Nested logits are used to model the discrete choices of
household fleet size (i.e., ownership of 0, 1, 2, or 3+ cars),
vehicle type/vintage, and vehicle body mixes. These are
linked to a series of continuous vehicle utilization models.
Lagged operators and other devices are used to render these
models dynamic in the sense of capturing what the authors
refer to as "experience effects" and "expectation effects"
within the multidimensional choice process. The approach
shows what can be done today to improve the behavioral basis
of vehicular travel demand modeling given a suitable
69
longitudinal database. The authors used their modeling system
to generate a range of automobile demand and fuel use
scenarios at 5- to 7-year intervals for up to a 20-year period
(1985B2005). Policy variables analyzed were vehicle and fuel
prices, advances in vehicle fuel saving technology, and
socioeconomic changes which affect household demands.
Use of Household Budgets to Bound Travel Activity Estimates
A number of researchers have hypothesized that the
amount of travel we undertake is highly constrained on the
individual level. Proponents of this approach argue that the
total amount of travel people engage in is strongly
constrained by either time or money budgets. Such budgets,
the latter strongly related to available income, are claimed
to either remain quite stable over time for any given city and
population subgroup, or to change in clearly recognizable
directions as a function of a few independent variables. Such
constraints allow the amount of travel that a person or
household engages in to be determined by appealing to a simple
utility maximizing model, subject to budget constraints. For
example, the longer-term decisions faced by the household as
to how much total travel to engage in (that is, number of trip
generations H trip distances) is modeled in utility terms (U)
by Golob, Beckmann, and Zahavi (1981), as:
Click HERE for graphic.
The idea here is that by maximizing over the expenditures
on time and money themselves, the longer term relationship
between the travel and nontravel budgets (leisure time,
consumption of other goods) of the household can be explored.
If stable relationships between such budgets can be shown over
a number of years, then we would have a very useful approach
for placing reasonably tight bounds on the total amount of
travel consumed. Zahavi and colleagues used such an approach
to develop the Unified Mechanism of Travel (UMOT) to
investigate empirically such hypothesized relationships
(Zahavi, Beckmann and Golob, 1981; Zahavi, 1982). While
conceptualized at the level of the individual household, the
supporting empirical modeling is carried out on an aggregate,
urban areawide scale.
70
However, the empirical evidence to date has been less
than conclusive, and no operational models based on household
travel budgets have been generally adopted (although as noted
in Sect. 2.5, the Dortmund model does incorporate a travel
budget constraining procedure within its transportation
modeling process). Household stratification along at least
car ownership and income lines would appear to be required if
reliable forecasts of future budgets are to be made. Also, to
be strictly applicable, nonmotorized modes of travel (walking,
cycling) need to be included in the analysis. It may also be
argued that people actually seek to maximize their
accessibility to opportunities, rather than seeking to
maximize their (budget constrained) distance traveled. Since
maximizing accessibility to a set of spatially diverse
opportunities need not involve minimizing distance or cost of
travel, the latter is really a special case of the former.
Various other pros and cons of a travel budget based approach
are reviewed by Gunn (1981), Wigan and Morris (1981) and
others in the same volume of Transportation Research.
In the most general terms, the notion of dealing with
travel distance (VMT) as the result of a budgetary process
appears to have considerable behavioral merit. Further
empirical study is needed to determine if a travel budget-
based approach can be developed directly into an effective
forecasting mechanism. Rather, it may offer a check on the
economicrealism implied by otherwise unconstrained modeling
approaches. To be most useful such investigations should,
however, be time-series as well as cross-sectional in nature.
3.3.2 Urban Goods Movement Modeling
A second area of deficiency in current practice is the
underdeveloped treatment of urban freight modeling. Attention
to the behavioral aspects of urban goods movement (i.e. to the
logic behind shipper and carrier operations) has seen little
application at the fully urban scale (UMTA, 1982). If the
behavioral waters of personal travel demand analysis are
murky, then those associated with freight generating business
practices appear downright obscure. Limited effort to date
has gone into determining the relationships between company
logistics and management practices and their effects on either
the daily scheduling and use of multivehicle fleets or on the
longer term decisions of where to (re)locate factories and
offices with respect to customers and existing freight
terminals (including some quite large break-bulk terminals).
Urban trucking research has, as we might expect,
dominated what literature there is in urban goods movements.
The most comprehensive attempt to come to terms with this area
to date was carried out by Transport Canada (1979), which
produced a multivolume report on various aspects of urban
freight movement. This work includes calibration (to Vancouver
data) of an urban truck transport model which links
traditional forms of trip generation, distribution, and
shortest path-based traffic assignment models to a truck load
consolidation model which "consigns" freight to trucks of
different sizes.
71
Such consignments are based on effective vehicle weight
capacity, the maximum daily hours of operation
(industry regulated), and the expected dwell times at pick up
and delivery sites. To allocate this consigned traffic
between inner and outer city traffic zones, an optimization
based model was used to determine whether to route this
traffic directly or via a freight consolidation terminal.
At the strategic, urban areawide level, Southworth, Lee, and
Zavattero (1986) also examined the efficiencies involved in
the use of alternative primary truck route designations and
the clustering of freight terminals within the Chicago
metropolitan area. Their approach and its empirical
application embeds circuit based measures of locational
accessibility within spatial interaction models. They also
propose a method for using the resulting flows within a mixed
person-freight traffic assignment model which in turn could be
used to compute fuel use and emissions. Recently, Oppenheim
(1993) has proposed an interesting and improved urban-areawide
formulation of a combined personal and freight equilibrium
traffic assignment model. Among other useful studies, the
recent work on freight logistics by Daganzo (1991) and Hall
(1993) consider the design of local area freight networks.
However, such efforts again reflect a series of largely
independent studies, focused on very specific aspects of urban
freight travel. A suitably rewarding conceptual framework
for urban goods movement analysis remains to be defined.
As with personal travel, much intraurban trucking is also
known to involve highly circuitous, multistop daily routing
activity (Southworth, 1982a). Circuit-based transportation
costs have been used within logit models of destination choice
similar in form to those used in passenger travel modeling
(Southworth,1982b, using data from the Chicago region). An
interesting possibility, discussed by Wigan and Morris (1981),
is the application of a travel budget approach to freight
movements. This notion has appeal, since it is the activities
at pick-up or delivery sites that often dominate the urban
trucker=s daily time budget, and since we might expect very
imilar allocations of travel time to such goods movement
services across cities of similar size, given the highly
competitive nature of the industry.
Our major hope for projecting aggregate levels of freight-
creating industrial activity lies in the belief that
businesses follow recognizable profit-maximizing or cost-
minimizing development paths. As with the above developments
in household travel, freight movement models based on the
individual firm have moved into, among other directions,
logistic demand based (Sheffi, Eskandari, and Koutsopoulos,
1988) as well as constraint based, multi-criteria mathematical
programming forms (McGinnis, 1989). These models again place
transport costs (including freight rates) as one among a
number of important decision variables in activity pattern
(notably, truck route, and schedule) generation. McGinnis
(1989), for example, found that carrier reliability, transit
time, and shipment loss and damage experience could be more
important to shippers than freight rates when selecting a
articular carrier.
72
Finally, in addition to the above issues, we now also need
to address the impacts of just-in-time inventory/delivery
systems, electronic message transfers, and the increasing
interfirm as well as intrafirm coordination of logistics
apparently taking place within today's information society. A
major shift away from expensive warehousing costs to just-in-
time parts and product deliveries clearly has the potential to
increase vehicle miles traveled within a number of industries.
3.3.3 Modeling Transportation=s Continued Role in Urban
Development
3.3.3.1 Transportation Infrastructure Investment Impacts
Looking at longer-term responses in the form of site
(re)locations, nontransportation factors again loom larger
than traditional industrial location theory suggests. Both
intraurban personal and freight travel patterns are affected
by the location of such companies; the former through the
necessary daily journeys to and from work and, less directly
but with increasing significance, through the effects such
demands place on the rest of a household's typical weekly
activity patterns. As reviewed by Giuliano (1989), much of
the recently available empirical evidence supports the view
that transportation is at best only one of many determinants
in both a household=s or a company=s location decisions,
sometimes acting as a constraint on subsequent economic and
related land development without alone being a sufficiently
motivating reason to cause a change in current location.
dense transportation networks that the perhaps once more o
bvious relationship between new highway infrastructure and l
and development is much less straight-forward, at least within
daries of urbanized areas.
In an assessment of the influence of road investment on
economic development, Forkenbrock et al. (1990) reviewed a
number of studies that reached the following conclusions (also
listed by Parker, 1991):
. Transportation investment may be a necessary but
not a sufficient factor for economic development.
. The impact of highway investments today, with a
mature highway system, may not be the same as in
earlier periods.
. Relationships between highways and local
development one mainly by association - there is
little confidence that highways led to growth, rather
than vice versa.
. The economic development process is too complex
and the role of transportation is not likely to be
sufficiently dominant to allow causal relationships to
be established.
73
. Education, unionization, physical amenities, business
climate, energy, and tax rates define a region's
developmental prospects to a much greater extent
than do highways.
In an often quoted national study of beltway
(circumferential highway) impacts, Payne-Maxxie Consultants
(1980) found no consistent relationship between the presence
of such beltways and land use. Rather, land use impacts were
dependent on (1) overall local economic conditions, (2) access
to medium income or high income residential areas,
(3) availability of developable land, and (4) favorable local
zoning ordinances.
However, the empirical evidence suggests care in
reaching to too general a set of conclusions. For Texas
cities with over 4000 population, Buffington, et al. (1992)
found significant correlations between 67 bypass, loop and
radial highway improvements and the growth in employment and
wage rates for the period from 1954 to 1988. They also cite a
number of other studies reporting positive relationships
between highway investment and employment growth. Their
results may reflect the small size of many of the areas
defined as urban, plus a starting point in 1954, when our
urban and transportation systems were far less developed.
infrastructure may now be in place, if we are going to use our
models to project a similar distance into the future, then we
should at least recognize the possibility of similarly large
(if in practice very different types of) changes in
transportation=s future relation to economic development. How
we make use of our built structures is changing, if gradually,
with each important new laborsaving technology to come along.
Of the above nontransportation factors of significance,
the presence of suitably trained labor pools has become an
important concern for companies looking to locate, or relocate
a factory or office. A recent survey of 504 manufacturers in
North Carolina (Hartgen et al., 1991) provides an informative
empirical study. Transportation related accessibility
materials, and local access by road) were generally ranked
below labor factors (notably worker attitudes, availability,
and trainability), while other important factors included
quality of life (public schools, quality of area for raising
children), site and utility costs (electricity costs and
supply), and local tax rates. A question facing model
developers, therefore, is how to better incorporate or
recognize such nontransportation, nonaccessibility based
factors within future urban models.
75
3.3.3.2 Spatial Agglomeration of Activities
Factors in New Urban Center Formation
As Berechman and Small (1988) point out, many of our
newly emerging urban places are different in structure from
the classical city containing a radial highway network focused on a
centrally located CBD. As a companion, and apparently
necessary, corollary to the automobile-induced urban sprawl,
the location of suburban centers, their rates of growth, and
their mix of traffic generating land uses now represent a
central concern for urban land use planning (Orski, 1985; JHK
Associates, 1989; Garreau, 1991; Southworth and Jones, 1995).
Traffic congestion within and between industrial as well as
commercial and mixed use suburban activity centers is now also
a problem. The very benefits of location and agglomeration of
activities offered by a city's CBD, and which led to its
subsequent traffic congestion problems, are now causing the
more peripheral urban subcenters to experience their own
version of traffic related negative externalities; encouraging
us to ponder what the solution to such agglomeration
diseconomies might be and where this process is leading us
(Cervero, 1989).
Such "polycentric" urban development appears to be
occurring at a number of scales and is having effects on
ravel speeds, trip distances, and total travel mileage. What
are today seen as an expanding metropolitan area' s suburban
centers may tomorrow become small satellite cities in their
own right. It is possible also that the functional ties
between these satellite cities and the long established CBDs
will be fewer and different than they have been in past
decades. What is currently lacking in our operational models
is any in-depth analysis of how such subcenters originate,
develop, and perhaps eventually become smaller cities in their
own right.
Despite the now quite long and active history of urban
economic analysis, current operational models shed little
light on this process; using simple incrementalism or random
event generation coupled with spatial accessibility measures
to produce alternative development scenarios. Traffic
congestion would here seem to be an important indicator of
when, if not where, a new industrial park or mixed use urban
activity center is likely to be needed. Just where they
spring up, or which existing centers will continue to compete
successfully, is currently much less obvious.
More effort appears warranted here in at least two
directions. First, more work needs to go into understanding
the locational influences on basic sector industries,
including both heavy and light manufacturing industries.
second, a more in- depth understanding of both intraindustry
and interindustry dynamics is required. To move this process
forward properly requires that we recognize the influence of
locationally induced economies of scale on the site selections
of such Abasic@ industries. The POLIS model discussed
previously has made one start in this area. More in-depth
analysis is necessary.
75
Such economies of scale arise from placing relevant
resources in close spatial proximity to each other, thereby
improving the productivity of participating firms. Henderson
(1988) distinguishes between scale economies internal to each
industry and urbanization economies resulting from the general
increases in economic activity which occur as a result of
locating within a large city. Both are industrial production
economies of the types discussed by Mills (1967), who suggests
that cities form in an economy because of scale economies
resulting from
1. communications among firms, which enhance the
speed of adoption of new technological innovations
and/or reactions to changing market conditions;
2. labor market economies for both workers and firms
searching, respectively, for specific jobs and
specific skill combinations;
3. greater opportunities for specialization in firm (and
worker) activities; and
4. scale economies in the provision of intermediate
common inputs (docking facilities, warehousing,
power, etc.).
As Henderson points out, scale economies which result
from the interactions between different but related industries
are particularly difficult to identify, because spatial
agglomeration may occur without their presence.
Transportation cost savingshave been cited in the past as the
major reason for such spatial clustering.
More generally, locations sharing a number of traits
desired in common by a variety of firms may lead to the
formation of a mixed use suburban center. Such tendencies are
evident in the commercial and retail as well as the industrial
employment sectors. Within the retailing sector, what
Berechman and Small (1988) term Ashopping@ agglomeration
economies are clearly important. The development of multistore
shopping malls recognizes the attraction to consumers of one-
stop locations. These tendencies have been extensively
modeled over recent years, notably by Wilson and colleagues
(see, for example, Harris and Wilson, 1978; Wilson et al.,
1981) whoseexperiments in applying quite rudimentary dynamics
to spatial interaction models quickly throw up complex
temporal shifts in the locational advantage of retail stores.
The wider applicability of such ideas on dynamics to the
commercial/office building sector (see Pivo, 1990, for
example) or, with adaptations, to the manufacturing sector
also needs to be looked into (see Wilson,1987, for some ideas
on this).
In addressing this question of how activity centers
originate and subsequently grow, we also need to allow for the
long recognized transition of our economy from manufacturing
towards both service-based and, increasingly, information-
based industries. In tomorrow's cities the never entirely
satisfactory distinction between basic and nonbasic sectors is
likely to become less useful. With many locationally
footloose
76
industries emerging, just what constitutes the benefits of a
particular locational choice may be quite different from what
it was justtwenty years ago. A search for better theories
suggests a search of the wider literature on the nature of
both intraindustry and interindustry contacts, their types,
frequencies, and impacts on firms=locations. For example, the
emergence of network forms of organization both within and
between firms is discussed by Cooke and Morgan (1993), who
consider it to be a significance development in terms of not
only corporate strategy but also in terms of regional
development potential.
Our urban system model-based explorations into these
and industrial linkages have been largely theoretical to date,
and our efforts to make urban center formation endogenous to
the modeling process are still highly theoretical in nature
(see Berechman and Small, 1988). Clapp (1984), for example,
adapted the new urban economics bid-rent model to include the
effects of business contacts by a single agent, such as the
corporate headquarters of a single company, on the rise of
suburban centers. However, much more work is needed in this
area, with potentially significant payoffs in terms of model
realism.
Factors Affecting Travel Within and Between Urban Centers
There is also a need for a more normative approach to the
problem, which might lead eventually to more prescriptive
modeling efforts. As Dyett (1991) points out, neither current
urban economic nor locational accessibility based theories
provide much insight into how to best configure land uses at
the neighborhood and community scale. However, such designs
may prove to be an important source of personal travel
reduction. He suggests more work be done to establish whether
suburban mixed use centers can be designed to take advantage
of cost effective urban designs (or in older suburbs,
redesigns) which support walk, cycle, park-and-ride, transit
and paratransit options. The location of public buildings
(police, fire, city hall) as well as urban parks and other
open spaces would also be important components in such
designs. We must also pay more direct attention to the role
played by land developers within this process. They can
become key players in the creation of effective private-public
partnerships. For example, they have been active in the
adoption of a range of trip reduction ordinances (TROs)
through their participation on local Transportation Management
Associations in states such as California (Ferguson, 1990).
They have also been identified as important players in the
development of employer-based rideshare-supporting schemes
(Southworth, 1985a). However, our current land use models
contain little if anything to reflect the actual role and
motivations behind these developer activities (see Levy, 1990,
for an interesting discussion).
Some useful research on practical design specifications
for mixed use urban centers was recently sponsored by DOT
(Snohomish County Transportation Authority, 1989; Middlesex
Somerset Mercer Regional Council, 1993) and by the 1000
Friends of Oregon (Cambridge Systematics et al., 1992a). Each
of these U.S. studies key their
77
discussions to a specific type of land use arrangement known
as transit-oriented development (TOD), which is proposed as an
integral part of a neighborhood or urban center's planned
growth strategy.
Once urban centers have formed, movements between
them will naturally become increasingly important. In doing
urban center planning then, the type, location, and areal
extent of the suburbs surrounding these commercial or mixed
use centers, from which they draw their workers and customers,
should be explicitly recognized and accounted for. According
to the 1990 National Personal Transportation Survey (Hu and
Young, 1994), while average personal trip lengths (one way,
averaged over all purposes) increased from 8.7 to 9.5 miles
between 1983 and 1990, increases in travel speeds kept average
avel times relatively stable. A possible explanation for such
increased speeds is the growth in intersuburb, as oppose to
now an important component of urban development and should be
further investigated.
A recent empirical study of the travel patterns of Chicago
residents by Prevedouros and Schofer (1990, 1991) contains
some interesting findings. Using aggregate census data to
classify suburbs into growing versus stable suburbs, they
surveyed 1420 respondents to compare two low-density, growing
outer-ring suburbs with two suburbs selected for their higher
density, stability, and inner-ring location. Among their
findings (Prevedouros and Schofer, 1991): for both types of
suburb, average speeds for automobile work trips were
statistically similar for all but trips to the CBD. However,
average trip distances were noticeably higher on average for
those from the growing suburbs, resulting in residents from
these suburbs staying in traffic some 25% longer than their
counterparts from the stable areas. Among their other
findings of interest were the high dependence of suburban
females on the automobile, the substantial amount of
off-peak travel being engaged in, and the possible, if not
entirely clear, effects of an aging population on trip rates
in the coming decade.
What the above suggests is that an integration of detailed,
possibly design-oriented, models of suburban mixed use center
formation with a more spatially extensive, and highly
tructured socioeconomic analysis of intersuburban linkages
offers a useful approach to consider for further theoretical
and empirical development.
3.3.4 Simulation of Urban Dynamics
As discussed in Sect. 2.5, the simulation of increasingly
comprehensive urban dynamics is already quite evolved, and for
multiyear forecasting the use of static-recursive approaches
may be sufficient for most strategic policy making. However,
further study of system dynamics is both warranted and
arguably necessary for the following reasons. First, a number
of studies indicate that failure to consider such dynamics
explicitly may cause us to misinterpret the actual processes
of urban change.
78
Second, intriguing possibilities for more direct
representations of detailed traveler behavior now exist than
at any time in the past. By making use of microsimulation
methods in conjunction with massively parallel or vectorized
computers its now possible to generate tens of thousands of
daily activity patterns in a surprisingly short turnaround
("wall clock") time. To take advantage of this opportunity
the travel activity analysts needs to develop explicitly
dynamic equation sets when trying to represent the behavioral
responses of travelers.
Over the past two decades a number of research efforts
have addressed the issue of introducing dynamics more
explicitly into our urban systems models. These include a
number of efforts focussed on the evolution of all or parts of
complete urban systems, notably (1) the work by the Leeds
group in the United Kingdom based on catastrophe theory
(Wilson, 1981), (2) the work by the Brussels Group in Belgium
based on uses of self- organizing system's theory and micro-
simulation (Allen, Engelen and Sanglier, 1986), and (3) a
number of efforts, including research in France (Fournier,
1986) and Italy (Bertuglia et al., 1981), to adapt the urban
dynamics approach proposed by Forrester (1969) to real world
cities.
Wilson (see, e.g.,Wilson, 1987) offers the following
approach to tracking the change over time in the size of a
facility at location j, Wj, as a function of profit accrued
at that location. Let Rj = the revenue attracted to that
location, then profit at j is given as:
Click HERE for graphic.
79
Experiments with these sorts of equations for retail
activity location systems (Harris and Wilson, 1978; Beaumont,
Clarke and Wilson, 1981) show the potential for significant
oscillations in facility sizes, including possible jumps back
to zero floorspace in some Wj values when the value of (1+gRj)
in Eq. (52) is greater than 2.
One conclusion from such findings is the need for caution
in oversimplifying the assumptions involved in detailed travel
pattern and associated land use forecasts. A more positive
view of the picture is that such discoveries will allow us to
experiment with the robustness of alternative urban land use
and transportation infrastructure plans if we can find a way
to bring them effectively into our operational models. Wilson
(1987) describes a beginning in this process by formulating a
dynamical version of the Lowry model based on the above ideas.
These developments also help to tie the above described
spatial interaction approach more closely to economic
concerns. For example, consider the mathematical programming
version of the shopping model discussed in section 2.4.2
above, and repeated here for convenience:
Click HERE for graphic.
80
modern day highly computer intensive microsimulation
techniques appears worth pursuing. Of particular interest are
methods which can use difference equations to simulate the
dynamics involved in mixed use urban activity center formation
and decline. For example, can we use explicitly dynamic
equations to microsimulate the alternative temporal paths
available to individual (that is, synthetically constructed)
companies as well as synthetically constructed travelers? In
particular, can we simulate the effects of spatial
agglomeration of activities based on the mutual locational
benefits discussed above by microsimulating the passage of
information as well as goods and people between such
companies?
3.4 PRACTICAL ISSUES: TOWARDS MORE USABLE MODELS
A strong argument can be made that as far as land use-
transportation modeling efforts to date are concerned,
toolmaking is more advanced than theory. It would be
difficult to find an area of research that has drawn on a
greater variety of mathematical, statistical, and
computational methods in its search for empirical validation
and subsequent practical applicability. Yet the application of
many of these techniques is much lesswidespread within the
planning profession than might be expected. Few practicing
regional or metropolitan planners calibrate their own
multinomial logit models or experiment with alternative land
availability or density constraints as part of a nonlinear
mathematical programming exercise. Nor is the issue simply
one of technical training. In order to encourage practicing
planners to make greater use of the models which do exist, the
models need to be made easier to use.
If planners from more than one jurisdictional level (local,
metropolitan, statewide, or regional) can be brought together
by use of a common, easy to use modeling, possibly game-
playing software, then improved models could possibly be
transformed into consensus building tools, rather than the
seemingly arcane components of a planning process in which
only one or possibly two experts within any metropolitan
planning agency have anything to do with them directly.
Researchers have already developed a range of user
interfacing capabilities, including graphical interfaces, for
commercially available models such as MEPLAN. However, we
can go much further here, as evidenced by the use of highly
interactive, multimedia, decision-support aids in other
fields. The emergence of reasonably priced and generally
accessible geographic information system (GIS) software is
the latest step in this development of decision-support tools. By linking a
relational database management tool to software programs for
manipulating spatial primitives (points, lines, polygons),
adding the land use and transportation modeling subroutines
themselves, and building around all of these an easy to use,
map-based interface, we have the principal components of a
spatial decision support system (an SDSS). Ongoing
developments in the SDSS arena promise more effective
manipulation of both spatial and nonspatial data
81
elements, in the short term through the more efficient
selection of which computations to carry out via database
manipulations and which to continue to model through the more
context-specific algorithms (see, e.g., Lolonis, 1993). The
field of urban transportation modeling is only now beginning
to make use of such GIS tools (Prastacos, 1991; Hartgen et
al, 1993; Anderson, Kanaroglou and Miller, 1994; Spiekermann and
Wegener, 1994).
Current developments in database encapsulated software
systems and object-oriented programming languages also suggest
a move towards more flexible software systems (Stevens, Tonn,
and Southworth, 1994). Again, we are just beginning to make
use of such advances in the field. For example, the SUSTAIN
model, an offshoot of the TOPAZ efforts at CSIRO in Australia,
is currently being developed as an object-oriented programming
approach specifically directed at the linkages between
transport, urban form, and energy consumption (Roy and
Marquez, 1993).
A key component of such decision-support systems will
be their ability to help the planner resolve often competing
energy, environmental, fiscal, social, and economic goals.
Here the use of multicriteria decision-making methods are
also worth further explorations. Three increasingly popular
decision aides that have been applied recently to
transportation project assessments include Saaty's Analytic
Hierarchy Process (see Zahedi, 1986 for a review), Roy's
ELECTRE III method (see Roy, Present, and Silhol, 1986 for an
application to Paris metro station locations) and Concordance
Analysis (see Giuliano, 1986b, for an application to the
ranking of alternative highway, bus transit, ride sharing and
commuter rail investment projects in Orange County,
California).
Each of these approaches can be applied to, actually on
top of, the outputs of any of the above reviewed land use-
transportation models as a further aid to strategic as well as
project specific decision making. In particular, they can
enhance the evaluation of trade-offs between the costs of plan
implementation and the energy savings, emissions reductions,
and the economic and social impacts (including intercommunity
equity impacts) of proposed travel reduction strategies. Any
realist hope for the acceptance of transportation plans which
significantly reduce petroleum based fuel use and greenhouse
gas emissions will certainly require plans which also address
such trade-off issues (see Bae, 1993).
There are a number of additional benefits to developing a
highly interactive, multimedia approach to decision support.
It's much easier to remain skeptical about a batch driven
process in which the user=s only interaction of note is data
input than it is to feel the same way about a process in which
he or she is an active component. Highly interactive user-
centered planning tools can prove to be very powerful
decision-support aids. This is particularly true for
spatially explicit problems. Here an interactive game of
consequence analysis is appealing; a game in which the analyst
gets to experiment with different starting points, parameter
values, land use controls, pricing schemes, fiscal and other
constraints, and a range of differentially weighted plan
objectives. A major benefit
82
from the use of such interactive systems is likely to be the
knowledge both gained from and added back to the system by the
analysts. Tomorrow's decision- support systems are likely to
combine text, and geo-graphics with sound, and video,
including animation (see Wiggins and Shiffer, 1990, for a
discussion). Software which allows the user to place aerial
photographs behind model constructs, such as network upgrades
and new land use arrangements, can also serve the process of
bringing the art of modeling closer to the planner. With the
arrival of global communications in the form of the Internet
and World Wide Wed, a considerable increase in the use of
interactive, map-based editing tools should be expected to
inform the land use and transportation planning process.
Indeed, in coming years, whenever a major metropolitan
planning proposal is transmitted in digital form its likely to
encourage a significant number of similarly transmitted
responses from a growing number of interested parties. This
constitutes a type of information which our present models and
supporting databases are perhaps ill-equipped to handle.
The development of such interactive, model-
encapsulated analysis tools constitutes a challenging research
and development task. Well-designed decision-support system
require as much thought (and at least as much money to render
operational) as do the computer models of land use and
transportation interaction on which they are based. The tools
now exist with which to build such software. However, current
commercial GIS packages are still some way from being the
spatial decision-support tools we need. Experience with such
software in the field of urban and regional transportation
modeling has been quite limited to date. Education in how to
construct, adapt, and use such software tools is now required
within the transportation planning profession.
Early efforts in this area currently include those at the
Universaty of California at Davis (Johnston 1995).
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