Final
Evaluation Plan
PDF Version 373KB
Prepared for:
ITS Joint Program Office, HOIT-1
by:
Battelle Memorial Institute
TABLE OF CONTENTS
Page
5.3 Implication for Evaluation Design
6.1 CP System Performance Test
6.1.2 Hypotheses and Measures of Effectiveness
6.1.4 Data Analysis and Reporting
6.2.2 Hypotheses and Measures of Effectiveness
6.2.4 Data Analysis and Reporting22
7.1
Evaluation Schedule and Deliverables
7.2
Evaluation Management Structure
LIST OF FIGURES
Page
Figure 3-1.
Pictures of CP System in Operation................................................................................ 3
Figure 4-1.
Overall Evaluation Process.............................................................................................. 5
Figure 5-1.
TRAX System Map........................................................................................................ 8
Figure 7-1.
Evaluation Schedule and Deliverables............................................................................ 24
Figure 7-2.
CP Evaluation Management Structure........................................................................... 25
Figure 7-3. CP Evaluation Work
Breakdown Structure................................................................... 26
LIST OF TABLES
Page
Table 6-1. Objectives, Hypotheses and
Measures of System Performance........................................ 13
Table 6-2. Anticipated Benefits, Hypotheses,
and Measures of Satisfaction....................................... 18
Table 7-1.
Estimated Level of Effort................................................................................................ 27
Table A-1.
Number of Protected and Unprotected Trips on a Given Weekday
for Each
Table A-2.
Distribution of Weekday Bus Trips by Time of Day vs. Frequency
of Bus Route................................................................................................................ 29
Table A-3.
CP Messages Generated During November and December 2002.................................. 29
Table A-4.
November/December CP Messages Generated for Buses at
Each TRAX Station..................................................................................................... 29
Table A-5.
Late Train Trips During November and December by TRAX Station............................. 30
Table A-6.
Late Train Trips During November and December by Day of Week.............................. 30
Table A-7.
Late Train Trips During November and December by Time of Day................................ 30
Table A-8.
Distribution of Bus Trips by Time of Day vs. Frequency of Bus Route............................ 31
Table A-9.
Potential Bus Trips that Could Be Used for a “With vs. Without”
Evaluation................... 32
Utah Transit Authority (UTA) implemented a Connection
Protection system (CP) to improve the reliability of transfers from the higher
frequency light rail trains, TRAX, to the lower frequency bus services. The CP system examines the status of TRAX
trains and issues a “HOLD” message to buses waiting at the connecting rail
stations via the onboard Mobile Data Terminal (MDT), if the lateness of train
is within a pre-determined threshold (e.g., 3 minutes). The system was completed and tested in
January 2002 prior to the Winter Olympic Games in
The successful implementation and operation of the CP system has received attention from the Federal Transit Administration (FTA) (the funding agency) and USDOT Intelligent Transportation System (ITS) Joint Program Office (JPO) to warrant a national evaluation study. The objectives of the evaluation are to assess and document the success of CP system and share the experience and lessons learned with other agencies that may be considering a similar system.
Battelle Memorial Institute was selected in September 2002
to conduct the evaluation. Brigham Young
University (BYU) in
This evaluation plan is the first in a series of deliverables to be developed by the evaluation team. The evaluation plan includes the background and the objectives of the evaluation, a description of the CP system, the evaluation approach, results of an exploratory analysis, discussion of the proposed evaluation tests, and an evaluation management plan, including estimated level of effort, management structure, schedule and deliverables. Following the acceptance of this evaluation plan, detailed plans for the individual evaluation tests will be developed. This evaluation plan is organized as follows:
A list of objectives for the evaluation of the CP system was initially identified by the ITS Joint Program Office (as part of the Statement of Work). This list was later reviewed and enhanced by the evaluation team and the UTA CP project team in the November 2002 kickoff meeting and in subsequent discussions. The objectives of the evaluation are as follows:
In general, the above objectives support two types of evaluation activities. A quantitative analysis using UTA system operation data including transfer data (e.g., CP message logs, bus arrival and departure times, train arrival times, etc.) will address objectives 1 through 6. A qualitative analysis will address objectives 7 through 9, using surveys and interviews with passengers, operators, supervisors, and analysis of archived customer comments.
These evaluation objectives are explored in greater detail in Section 6: Proposed Evaluation Tests.
UTA’s Connection Protection system is designed to improve the reliability of transfers from the light rail train, TRAX, to the connecting buses on selected bus routes. The CP system constantly monitors the train schedule adherence status by examining the estimated arrival times for the next 3 stations. If the lateness of a train is within a pre-determined threshold (e.g., 3 minutes), a “HOLD” instruction is sent to the connecting buses via the onboard Mobile Data Terminal (MDT). The relatively short 3 minutes threshold was used to avoid possible adverse effects on the bus on-time performance.
The merit of UTA’s CP system is the intelligent integration of a number of existing systems and data, which greatly reduced the project’s capital cost. For example, the estimated train arrival time data are produced for real-time train status display at the light rail stations by a subsystem developed by Geo Focus. The transmission of “HOLD” instruction messages is achieved using the existing Mobile Data Terminal (MDT) as part of the voice/data radio system equipped on all UTA buses. The CP messages are sent the same way as other data messages from the dispatcher to the bus operators but without human intervention. The major capital cost of the CP system is the cost of the computer server and the interface peripherals with other systems. Figure 3-1 depicts related components of the CP system.
Figure 3-1. Pictures of CP System in Operation
The CP system is essentially a computer program that takes relevant data (e.g., estimated train arrival time, train schedule, bus schedule) from other systems and determines if a late train would jeopardize the bus connections at downstream stations. When a potentially missed connection is identified and the delay of the train is within a threshold (e.g., 3 minutes), the CP system looks up the information of the particular bus (using the bus schedule) and sends a “HOLD” message to the onboard Mobile Data Terminal (MDT) using UTA’s radio dispatch communication system.
The CP system is flexible in terms of which transfer location and time to protect. For example, a bus route can be protected only for a certain period of the day. In addition, internal parameters such as the threshold of train lateness (typically 3 minutes) can also be adjusted by individual transfer location and time. For example, to protect the connection of a last bus, the threshold might be increased in order to hold the bus longer until the train’s arrival.
This section describes the overall approach taken toward the evaluation of the UTA CP system.
As suggested in the Statement of Work, the quantitative assessment of the effectiveness of the CP system is meant to be a “with” and “without” comparison using transfer locations that are currently under CP system protection and similar locations without CP system protection. The reason for using a “with” and “without” instead of “before” and “after” evaluation design was the lack of useful system data (e.g., train arrival times and bus arrival and departure times) before the implementation of the CP system in January 2002.
In order to better understand how the CP system works and to identify the possible “with” and “without” evaluation scenarios, the Battelle evaluation team conducted an exploratory analysis using system data provided by UTA. The results of the exploratory analysis were used to guide the development of this evaluation plan and the detailed test plans that will follow. Figure 4-1 shows the overall process employed by this evaluation.
Figure 4-1. Overall Evaluation Process
The general evaluation approach of the quantitative study of the effectiveness of the CP system lies in the comparison of a number of key system operation data, including:
The CP message logs and train data are being systematically archived by the UTA. Bus data are not available system-wide, as UTA does not have an Automatic Vehicle Location (AVL) system on its buses that would provide vehicle tracking (in time and location) capability. However, arrival and departure time information is available from the Automatic Passenger Counter (APC) equipped “smart buses” that constitute about 15% of the fleet. The Automatic Passenger Counter is used in conjunction with the Global Positioning System (GPS) to determine the time and location of the passenger counts and the arrival and departure times at each stop. The logistical challenge is to allocate the smart buses of various vehicle types to the selected evaluation routes during the data collection period. UTA has agreed to provide all smart buses, including the 60 new buses procured in early 2003, in support of this evaluation. Specific arrangements will be made as part of the development of the detailed test plans. Based on the exploratory analysis, Battelle concluded that the CP message logs, and the train and bus data provided by the UTA are sufficient for conducting the analysis in support of the evaluation objectives described in Section 2.
The general approach for the proposed qualitative assessment is relatively intuitive. A survey is envisioned to collect passengers’ perceptions of connection reliability at transfer locations with and without CP system protection. The selection of with and without CP transfer locations for the passenger survey will be coordinated with the quantitative study, based on the results of the exploratory analysis. The challenge of the passenger survey is to recruit the subjects without jeopardizing their transfer and the rest of their journey. A number of possible intercept survey techniques were explored by the Battelle team during and after the evaluation kickoff meeting. The method and logistical arrangements will be identified with BYU staff and as part of the development of the detailed test plans. Other qualitative analyses will be based on key informant interviews with bus operators and supervisors, and logs of customer comments (complaints) with regard to connection experience.
More discussion of the specific evaluation activities is provided in Section 6: Proposed Evaluation Tests.
To assist in the
development of an appropriate “with-without” evaluation design, Battelle
conducted an exploratory analysis using data collected by UTA during the last two
months of 2002. The purpose of this
analysis was to summarize which bus trips were CP protected, which ones received
the most messages, and which TRAX stations had the most instances of late
trains. An interim technical report
covering the results of this exploratory analysis was delivered to DOT on
Five primary
objectives, listed below, were defined for the exploratory analysis:
UTA has systems in
place to collect data related to the performance of the CP system. Collected data include arrival and departure
times of buses and trains, a log of CP messages that are generated, and
databases of bus and train schedules (including indications of which bus trips
have been assigned CP protection). The
exploratory analysis made use of data collected between April 2002 and January
2003. Note that three “change periods”
(i.e., tri-annual schedule and bus route adjustments to respond to seasonal
changes in travel) occurred over the time span covered by the data. This summary of the exploratory analysis
focuses on data from the last change period. Additional analyses on the other two change
periods are discussed in the full exploratory analysis report delivered to DOT
on
Bus and train times
were grouped into four time-of-day categories for some of the analyses. These categories were defined as:
AM rush =
Mid-day =
PM rush =
Evening =
Smart-bus wait times
were calculated by subtracting the bus arrival time from the bus departure time
at each stop. These arrival and
departure times are logged by the GPS equipment on each bus when the bus passes
by pre-defined points (i.e., independent equipment from the APC counters that
count passengers).
Tables showing the
results of the various analyses are provided in the appendix (Tables A-1 –
A-8). Figure 5-1 provides a UTA TRAX
system map to facilitate the interpretation of the analysis results. A discussion of the results is provided
below.
Figure 5-1. TRAX System Map
Protected Trips
The TRAX system is
comprised of two lines: the University
Line, which runs east/west between downtown and the
Excluding the
University Line and downtown TRAX stations, there are 1,227 possible weekday
bus trips (unique route/station/time combinations) per day that could be CP
protected (CP protection was not done on weekends). A total of 77 of these “daily” trips were
protected during Change Period #3, representing 6% of the possible weekday trips.
Thirty-nine
different bus routes intersect the TRAX stations of interest (including eight
routes that intersect multiple stations).
Only 15 of these routes had some of their trips protected by the CP system. Table A-1 shows the number of protected and
unprotected trips on a given weekday for Change Period #3. A list of the routes where CP protection is
implemented (along with their corresponding TRAX stations) is given below:
The vast majority of
trips that were CP protected were PM-rush trips (59 out of 77). Twelve, four, and two trips were CP protected
during the AM-rush, mid-day, and evening, respectively. Table A-2 shows the number of protected and
unprotected trips by time of day and frequency of bus route.
CP Messages
Four types of CP
messages are generated by the CP system and recorded in the message log that is
maintained by UTA. These include messages
that are transmitted to appropriate buses (“Hold” messages); messages that are
generated too late to be transmitted, as determined by the pre-defined
thresholds (“Missed” messages); messages that should be transmitted but cannot,
because the bus driver has not logged into the system correctly or some other
problem has occurred (“Bad Transmission” messages); and repeat messages that
are generated because of increasingly late trains and are not transmitted
because the bus has already been instructed to hold (“Discarded” messages).
During November and
December 2002, there were 41 possible days of travel (excluding Thanksgiving
Day and Christmas Day, when there was no service; and weekends, when no trips
were CP protected). Given that there are
77 protected trips each day, that yields a total of 3,157 possible protected
events for this two month period. A
total of 1,415 CP messages were generated during this time period; however, 897
of them were either bad transmissions, or missed or discarded messages. Table A-3 summarizes the 1,415 CP messages by
message type. The 518 “Hold” messages transmitted
during this period implies that 16.4% of the connected bus trips received
“hold” CP messages during this period (518/3157 = 16.4%).
Excluding the 558
weekend “Bad Transmission” messages, there was an average of 20.9 CP messages
per day (non-holiday weekday) in November and December. However, there were four days (9%) during
this time when no messages were generated.
Thus, on those days when at least one CP message was generated, there
was an average of 23.2 CP messages per day.
In November and
December, buses at Millcreek, Historic Sandy, and
Late Trains
Trains that were at
least three minutes late were summarized according to TRAX station, time of
day, and day of week (see Tables A-5, A-6, and A-7). Trains were more likely to be late in the
evening and afternoon rush, and on Mondays, Fridays, and Saturdays. Also,
The degree of
lateness for these trains ranged from 3 minutes through 600 minutes, with an
arithmetic mean of 10.5 and a median of 4.
No major differences were seen in the lateness of trains at each of the
TRAX stations.
Smart Buses
Over 9,500 trips
were made by smart buses in November and December. On over half of these trips (56%), the buses
apparently did not stop to drop off or pick up passengers at the TRAX station
(i.e., the arrival and departure times were equal). Two sets of statistics were calculated on the
wait times of the smart buses. The first
included all of the trips, while the second included only those trips that
actually stopped at the TRAX station. In
the overall case, the buses waited for an average of 3.4 minutes at each TRAX
station, while the average was 7.8 minutes if only those buses that stopped
were included. No major differences were
seen in the wait times at each of the TRAX stations.
Potential CP Assignments
In trying to
identify trips that could be used in a “with and without” comparison for the
field evaluation, the protected and unprotected trips were categorized by the
time of day that they occurred, the frequency between bus trips on a particular
route, and the TRAX station that they served.
This comparison is presented in Table A-8.
Listings showing
each individual bus trip that occurred in November and December then were
generated and examined to determine trips that were not CP protected and that
were similar in nature to protected trips.
The goal was to identify unprotected trips that were scheduled to depart
the TRAX station at least three minutes after the scheduled train arrival time
and no more than one minute after the corresponding CP-protected bus was
scheduled to depart the station. These
trips were considered to be potential “without” cases (subject to additional
input that UTA may have about them).
Table A-9 lists all of these trips and their associated protected trips
(including the number of “Hold” messages generated in November and December,
and the train time associated with each CP-protected trip). In summary, the number of potential “without”
cases at each of the TRAX stations for which cases were identified is as
follows:
It appears that
there may be an opportunity to conduct a “with vs. without” comparison of UTA’s
CP system. A total of 55 unprotected bus
trips were identified that occur in close proximity to bus trips that are
already CP protected. Additional trip
characteristics including type of service (local vs. express) and passenger
volume can be investigated to determine which trips would be best for inclusion
in the evaluation.
Two types of tests will be performed to evaluate the performance of and satisfaction with the CP system. The first one will be a quantitative analysis of the performance of the CP system, while the second one will be a qualitative assessment of customer and UTA satisfaction with the CP system. This section describes these two tests in further detail. Complete details on how they will be conducted will be provided in the respective detailed test plans.
An evaluation will
be conducted to determine how well the CP system is operating in regard to
preventing missed connections for train and bus passengers. The objectives of this analysis and the manner
in which it will be conducted are described in this section.
The primary objective of the evaluation of
system performance data is to evaluate the effectiveness of the CP system. More specifically, the objective is to evaluate
the ability of the CP system to prevent
missed connections and its ability to improve the number of successful
train-to-bus transfers for selected scenarios.
A second objective
of the evaluation of system performance data is to evaluate operational aspects
of the system performance. However, this
will not include an evaluation of the specific hardware and software that
comprise the CP system. Rather,
particular emphasis will be on evaluating the extent to which the CP system
consistently operates the way that it was designed from an overall perspective.
Finally, a third
objective of the evaluation of system performance is to assess unforeseen or
unintentional impacts resulting from implementing the CP system. In short, this portion of the evaluation of
system performance data will be conducted to determine if the benefits of the
CP system in terms of protecting riders have unforeseen costs associated with
them. For example, does holding a bus
via a CP message at one station cause missed bus-to-bus connections downstream
at later stops?
There are many
different measures that could be used to meet the objectives of the system
performance evaluation. Table 6-1
contains some key measures and example hypotheses for each of the three
objectives discussed above. These
measures and hypotheses will be refined as part of the development of a
detailed test plan.
Table 6-1. Objectives,
Hypotheses and Measures of System Performance
Objectives |
Hypotheses |
Measures |
Sources of Data |
Assess the effectiveness of the CP System in reducing the
number of missed connections |
§
CP prevents missed connections that would otherwise be missed (i.e.,
CP increases the overall number of successful connections) §
CP prevents missed connections in extreme situations (e.g., last bus
of the day) §
There are no bus trips that are not being currently protected that
should be (i.e., cases where CP could have a significant impact) |
§
Number and percentage of missed connections §
Average bus wait time §
Number and percentage of late train events that would trigger a CP
message §
Bus arrival and departure time §
Train arrival time |
§
CP Message Logs §
Smartbuses §
Train logs |
Assess the operational aspects of the CP system,
not to include software or hardware |
§
Messages are being issued when they are
supposed to be §
When a message is issued, drivers receive
the message §
Drivers do not ignore the messages §
CP messages are targeted for the correct
trips |
§
Number of messages issued vs. expected §
Number of messages that were not received §
Number of messages that were erroneously
sent |
§
CP Message Logs §
Train logs |
Determine if there are unanticipated impacts as a result
of implementing the CP system |
§
CP does not increase safety risks (i.e., drivers do not travel at
faster speeds to “make up” time lost from a CP hold). §
CP does not have lasting impacts on bus routes (e.g., buses are back
on schedule within a few stops of the CP hold) §
Buses have greater occupancy when CP messages are issued than without
(more cost effective) |
§
Bus occupancy §
Number of “on-time” arrivals of buses §
Average wait time §
Number of missed bus-to-bus connections |
§
Smartbuses |
Data will be
collected for a three-month period from September through November of 2003,
plus an additional month if necessary.
Most of the data required for the collection of system performance are
already routinely collected as part of UTA’s normal course of operations. Therefore, we anticipate that some system
performance data will be available for months proceeding the field period. We will capture and use as much of this earlier
information as possible. This data will
be used to provide more accurate measures of average wait times, bus occupancy,
frequency of late trains, and the number of CP messages that are issued.
There are a number
of different sources of data that will be used to evaluate system
performance. The data that will be used
for the evaluation of system performance include:
As discussed above,
all but one of these data sources are developed as part of the normal operation
of the CP system. However, it will be
important to capture the information for the evaluation before it is
inadvertently erased or recycled as part of daily operations. With bus arrival and departure times, there
may not be sufficient data collected as part of routine daily operations to
facilitate a robust analysis. Should it
prove necessary, we would collect supplemental information on bus arrival and
departure times by asking the bus drivers to record these times on a log
sheet. Existing log sheets would be used
if available.
An ideal approach
for examining system performance, as it can be used to measure the
effectiveness and other impacts of the CP system, would be to use a “before and
after” approach over comparable time periods.
In this approach, each bus trip (i.e., unique route/location/time combination)
serves as its own control. An
alternative to this approach would be to employ a “with and without” approach,
where comparable “without” cases serve as controls for the “with” cases. The first approach requires an assumption of
comparable time periods, while the second approach requires the assumption of
similar “with” and “without” bus trips.
For this evaluation,
a multi-faceted approach will be used to evaluate the system performance
information to assess the effectiveness of CP on reducing missed connections. The following describes the facets of this
approach. In all cases, except where
noted, the key outcome of interest is the number or percentage of cases where a
missed connection was identified.
Analysis of “With” and “Without”
UTA has indicated
that the trips that tend to be CP protected are those with longer intervals
between buses or are during the evening rush hour or at the end of the
day. Most of the CP-protected trips are
at Historical Sandy and
Implementation of this approach would consist
of collecting system data for all trips that are currently being CP protected
(i.e., the “with” cases) and for a selected number of trips that are not
currently being CP protected (i.e., the “without” cases). Overall, data will be collected
simultaneously from those trips that are being protected and those that are
not. This data collection activity will
occur over a three-month period.
Analysis of “Before” and “After”
Estimates of
the number or percentage of missed connections could be compared for some of
the trips that are not currently being CP protected (“Before” cases) to similar
estimates once CP has been activated for those trips (“After” cases). This has the advantage in that each trip
serves as its own control, which increases the ability to statistically
determine the impact of CP on these locations.
The difficulty in this approach is that these are trips that are of a
“lesser” concern, which is why they were not CP protected in the first
place. However, if CP can be shown to be
effective for these trips, then this would be strong evidence that it was
likely effective for the routes already CP protected, though the converse would
not necessarily be true. Analysis of
variance and logistical regression techniques similar to those employed for the
With-and-Without analysis also will be used in this statistical analysis.
This approach
will be utilized for the two new bus routes being added to the Ballpark
station, as well as for any other routes that can be identified. Information on these routes will be collected
for a period of approximately 1.5 months without protecting any trips. Then, the CP system will be turned on for
trips on these routes and data will be collected for another 1.5 months.
Analysis of Average Wait Times
Late train events
will be examined for the trips that are CP protected. For each event, a determination will be made
as to whether a missed connection was avoided because of the CP system. A historical average wait time will be
applied to determine the likely outcome in the event that a CP message was not
issued. This analysis would seek to
answer questions such as: if the train
was estimated to be 4 minutes late and a CP message was sent to the bus driver to
hold the bus long enough for the train to arrive, would the driver have been
likely to wait anyway, based upon the historical wait times? The challenge with this approach is that is
relies upon applying a historical average to specific events and assumes that
there has been no systematic change in the average waiting times of bus
drivers. Ideally, information on the
average wait time for each trip would be available prior to the implementation
of CP. If this is not the case, then
this will be estimated using data from non-CP events at the CP protected trips
or from non-CP protected trips.
Analysis of Undeliverable Messages
There are a significant
number of “Missed” messages that could occur during the three-month field
period. For example, over the course of
a 153-day period in late summer/early fall there were 263 messages that were
“Missed” messages, which represents 16% of all messages that were issued. These messages represent cases where the bus
operator had not logged into the system or cases where the scheduled departure
time of the bus was before the CP message was issued. These cases will be examined to evaluate
system operation as well as effectiveness of the system. Because these types of messages are
uncontrollable, no action is needed to implement this method.
Analysis of Ancillary Impacts
Analysis of variance
and descriptive statistics will be used to examine issues such as impacts to
subsequent stops, speed, and bus occupancy.
In all cases, comparisons will be made between events where a CP message
was issued to events where a CP message was not issued. That is, this comparison would only be made
using CP bus trips and each bus trip would serve as its own control. For example, the lateness of the bus (with
respect to the schedule) at the next subsequent stop (time point) following the
TRAX station will be compared between CP and non-CP events. This analysis would give insight into whether
the bus driver tended to increase speed to “make up” for a CP hold.
This section describes the evaluation plan for the qualitative assessment of customer and operator satisfaction with the CP system. Complete and final details of the proposed evaluation tests will be provided at a later date after consultation with UTA and BYU staff in the form of detailed test plans.
The main objective of the qualitative evaluation is to assess traveler and bus operator satisfaction with the CP system. Specific objectives are expressed in the following evaluation questions:
To meet these objectives, the evaluation will involve a series of intercept surveys, with interviews of selected passengers on the UTA rail and bus system at specified transfer points to be conducted by trained and supervised BYU student interviewers. In addition, key informant interviews will be conducted with selected bus drivers, CP system operators and managers, and UTA customer comment logs will be examined and included in the analysis where appropriate. A final objective will be to add qualitative depth and interpretation to the system data being collected and analyzed under the system performance test.
Table 6-2 presents an initial understanding of the user satisfaction benefits, hypotheses and measures that will guide this component of the CP evaluation.
Table 6-2. Anticipated
Benefits, Hypotheses, Measures and Data Sources
Objectives and
Anticipated Benefits |
Hypotheses |
Measures and Data Sources |
Increase
satisfaction of transit users |
- TRAX rail riders perceive that bus connections are
reliable, and more reliable than they used to be |
- Stated preference surveys of riders (Source: in-person
interviews using intercept surveys at selected TRAX stations and on buses) |
Increase
satisfaction of bus operators, dispatchers, and transit system managers |
- Bus drivers are more satisfied
with connection performance |
- Key informant interviews using
structured questions (Source: selected bus drivers, dispatchers, managers) |
To evaluate user satisfaction with the connection protection program, data will be collected from various user groups by several methods that include the following:
The timing of the data collection period will need to take account of BYU’s staff and student interviewer availability, as well as avoiding UTA’s seasonal schedule changes. The data collection period is expected to be during September through November of 2003.
Site Selection. Two or three sites will be selected for the conduct of the intercept surveys. The underlying intent is to intercept and interview transit riders who travel on rail-bus routes that historically have experienced late trains and actual or potentially missed rail-to-bus connections. Rail-bus routes that meet these conditions will be further divided into those that have connection protection operating and those that do not. This with-without sample design will allow us to evaluate the role that CP plays in travelers’ experiences and satisfaction with transit service.
Two or three TRAX rail stations that experience the highest frequency of late trains but where only some of the bus routes/times are protected. This approach will allow for control of a number of factors that might influence customer satisfaction, including geography of the locations, some attributes of travelers, and trip timing. For this approach to be successful, we will need bus routes that are both protected and unprotected with schedules and headways that are similar. An advantage to this approach is the survey administration efficiency that is gained by having to supervise student interviewers at a few locations. Furthermore, this approach offers synergy with the system performance evaluation that intends to follow a similar evaluation design.
Rider Intercept Surveys. Data on rider satisfaction with the CP program will be collected through surveys with a sample of riders. This component of the test plan has several steps that include:
Battelle will work closely with the BYU survey supervisor and student interviewers to design and carry out the intercept surveys. The plan is to use trained BYU student interviewers to conduct intercept surveys with riders of the UTA TRAX and bus system at a pre-selected rail stations. The primary target audience for the survey is those TRAX rail riders who intend to connect with a bus at one of the rail stations along the rail route. These riders will be sampled over a one-week period by day of week and time of day. Details of the sampling plan will be developed based on information about bus routes, ridership levels, arrival/departure schedules, and the analysis of historical CP system performance data. The sampling plan will be included in the detailed test plans. An effort will be made to create a representative sample of rail-to-bus transfer riders who are exposed to missed connections so that conclusions from this evaluation can be generalized to the population of all train-to-bus transfer riders of the system. In addition, a smaller sample of bus riders who are on buses that may be subject to delays due to the connection protection system will be surveyed in order to evaluate any potential issues associated with schedule delay due to the CP program experienced on those bus routes.
We expect that many transit riders are, and will continue to be unaware of the existence of the CP program, and more specifically of whether or not a CP message has been issued that may impact their travel experience. That is, they are likely to only be aware of whether or not they successfully make their rail-to-bus connection, and not why they may have succeeded or failed, especially when their TRAX arrival at the connecting station is known to be later than scheduled. Given this, the survey will be able to assess rider satisfaction with the connection (and other) service attributes, and with rider awareness of the CP program, but the focus will be on evaluating differences in rider satisfaction with their connection experience since the introduction of CP program.
During the next site visit, Battelle will work with UTA and BYU to finalize the survey design, interview formats, and the sampling plan. Prior to conducting the surveys, students from the BYU Department of Engineering will be recruited and given an orientation to the project and interview training. They will be assigned to the selected transfer station(s) at specified times and instructed to intercept a sample of riders who are transferring from rail to bus. Both protected and unprotected transfers will be sampled, but the protected transfers will be over-sampled. Because transfer time is expected to be very limited, riders are expected to be in a hurry. In order to avoid a sampling bias associated with differential value of time by these travelers, transferring riders will be identified and intercepted either while boarding the bus or once on the bus. An effort will be made to identify all persons on the bus at the connecting station who have just transferred from the arriving TRAX train. Persons who arrived at the connecting station on the target bus, or who are initiating their bus ride from that station but did not arrive by TRAX, will be identified and a sample of them selected to complete a second (somewhat different) questionnaire.
Respondents will be asked if they would be willing to complete a short written survey (self-administered questionnaire), and handed a packet containing an official introductory letter requesting their support, a brief explanation of the purpose of the survey, a copy of the appropriate survey depending on whether they are transferring from rail to bus or not, and a postpaid mailer to allow respondents to mail back their completed questionnaire. However, the student interviewers will be riding the bus with the intent of collecting the completed questionnaires prior to the next bus stop. A toll-free phone number will be provided for answering any respondent questions or responding to concerns. An incentive will be offered to those who complete the survey, and the details of this will be worked out with UTA. For example, it may be possible for the UTA to offer one or more reduced fare trips in the future to riders who complete the survey, with the bus drivers handing these to riders who turn in their completed questionnaire packet before disembarking.
Because the connection protection program was initiated in January 2002, there is no opportunity to collect pre-deployment baseline data. However, two strategies are available to provide for with and without comparisons of traveler perspective on this program. First, questions will be asked of the target respondents regarding their travel experiences prior to connection protection and to assess changes they perceive with their travel experiences since the CP program has been in place. We will address potential issues of recall bias in this approach. Second, some riders will be intercepted and surveyed on buses that do not have connection protection. These routes will be selected in consultation with UTA, as noted earlier, to match as well as possible routes and conditions where the new service is now being provided. In addition, some riders are expected to have experience with both conditions, to the extent that they transfer from rail to bus at different locations, depending on trip timing and purpose, though they are unlikely to be aware of whether or not connection protection service was operating at the time of their various transfers.
Rider Questionnaire Design and Implementation. Survey questions will be developed and reviewed with UTA and BYU during the forthcoming site visit, and then pre-tested with a sample of riders. This will test for clarity, understandability, and content of the questions, as well as for the feasibility of the intercept strategy and the response rate that can be anticipated. It also will constitute a dress rehearsal for the student interviewers. Refinements will be made before implementing the final survey plan. Sample size targets will be specified in the detailed test plan but are anticipated to be sufficient to achieve about 300-400 completed surveys.
Detailed rider survey questionnaires will be constructed to obtain the kinds of information listed below. However, because of the need to keep the questionnaires fairly short, these questions are presented as potential items for further consideration and prioritization. Most, if not all, of the questions will be presented as closed-ended, offering check box response categories to assure clarity and ease of completion. Draft mockup questionnaires will be prepared and reviewed with the BYU team and refined in a more final draft form.
· How often do you ride the bus? Light rail?
· How long a UTA rider (new, old, number of months)?
· Frequency of transit riding? Trips per week/month?
· How often transfer from rail to bus? At this location? Others?
· Time of day; day of week of transfer?
· Do you use rail/bus to commute? How often? Arrival/departure flexibility? Length of commute? Total number of transfers on your commute?
· Purpose(s) of trips? Commuting, pleasure/sightseeing/recreation, personal business/errands, other?
· Availability of alternative means of transportation? Have access to or own car?
· Is rider transit dependent or ride by choice?
· Perception of on-time performance? How often off schedule? Perception of any changes in experience before and after protection implementation?
· Experiences missing a connection from rail to bus? At this location? Others? Time of day? Day of week? Frequency? Perception of connection reliability?
· How important is it to you to be certain of connecting to the next bus? What is typical wait time if miss bus connection at times you travel?
· Attitudes (concerned about safety/security at transfer center, worry about being late, delay tolerance).
· Ever submitted a complaint to UTA? Current concerns or complaints related to experience with connections from rail to bus?
· Prior awareness of connection protection system from media or elsewhere?
· How does system benefit rider? Impact on riding behavior? Effect on riding frequency?
· Suggestions for improving the system?
· Demographic background (gender, age, education)
Operator Surveys and Key Informant Interviews. Working closely with UTA, we will obtain a list of key informants for surveying and interviewing. These will include drivers of buses on which connection protection has been implemented (and some where it has not been implemented), system supervisors, dispatchers, and managers or others who are in a position to offer perspective on how well this system is perceived to be working and to articulate in their own words the benefits or impacts of the system as they understand them. Given the large number of bus drivers in the UTA system, a sample of all drivers will be interviewed, but an effort will be made to interview all the other key informants. Interviews may be arranged individually or in groups for efficiency. If several individuals are interviewed together, they will be of the same type (drivers, supervisors, etc.), and the interviewer will exercise care to be sure that each individual can express his or her own opinion.
In addition to interviewing a sample of bus drivers, it is expected that a short survey will be distributed to all the bus drivers in the system, coded in order to track which routes are covered and which drivers have connection protection experience. This will allow us to collect a consistent set of data from all drivers who respond to the survey. The drivers will then be sampled and selected into several interview groups to allow more detailed inquiry into the issues associated with the connection protection program and their practical experiences with it. These discussions will go into greater depth than is possible with the short survey questionnaire.
Complaint Logs. In addition to the survey data, comment/complaint logs will be obtained from UTA and analyzed in terms of the issues raised that are potentially pertinent to the connection program. The complaint categories will be reviewed, and UTA will be requested to provide selected information on the nature and details of the complaint for those complaint categories that are likely to be related to transit riders seeking to connect from rail to bus. No information that would reveal the identity of the individual filing the complaint will be requested or provided. Thus, these will be anonymous records of the complaints. Coverage of the complaint logs is believed to go back several years.
Data acquired from the surveys and interviews will be coded, manually entered into an MS Access database, verified, and cleaned. Depending on the sampling strategy selected for identifying intercepts, the data may need to be weighted prior to analysis in order to provide representative results. Analyses will be conducted using SPSS[1] to generate marginal frequency distributions for all the variables in the survey. Relationships among the variables will be explored using cross-tabulations, analysis of variance, or multiple regression, as appropriate, and the results will be presented and interpreted. Where possible, rider perceptions will be compared with system performance data that are discussed in the system performance test plan. This may include, for example, rider perceptions of system delays with objectively measured deviations from schedule. BYU offers no-cost statistical support to all its research departments and will support this team on such statistical details as determining a proper sample size; selecting locations, times and respondents for the intercept surveys; designing a data weighting scheme, and developing analytic strategies. Tabular and graphic data will supplement the report narrative to present results in a clear and understandable way.
Where possible, the complaint log files will be coded by bus route and location. It is recognized that these data are anecdotal and may not be amenable to formal statistical analysis, but rather will offer additional qualitative understanding of issues related to connection protection. Also, these logs will be content-analyzed early on in the research process and mined for issues and ideas that can help shape the structuring of the surveys and interviews. To the extent possible, after inspecting the available data, comparisons will be made station by station (or bus route by bus route) to look for differences in issues and differences between protected and non-protected bus-rail connections. That is, depending on our ability to determine the circumstances to which these complaints apply, they will follow the with-without comparative approach. Also, they may be amenable to a limited before-after analysis using the time stamps on the log files.
Figure 7-1 presents the evaluation schedule and major
deliverables. UTA has recommended that the
system performance data collection be conducted during the fall schedule change
period between
Figure 7-1. Evaluation Schedule and Deliverables
The management structure of this evaluation is presented in Figure 7-2. The evaluation team is managed by Dr. Jeffrey Jenq of Battelle who reports to Mr. Terrell Williams of Federal Transit Administration (FTA) who serves as the Federal task manager for the CP evaluation. Mr. Williams reports to Dr. Joseph Peters of ITS Joint Program Office (JPO) who is the manager of the ITS Program Assessment (IPAS) program. Mr. Williams is supported by Mr. Steve Mortensen of Mitretek who provides various evaluation supports to the USDOT. Dr. Jenq also will coordinate with the UTA evaluation committee through the ITS Project Manager, Mr. Richard Hodges.
Dr. Chris Cluett of Battelle serves as the qualitative study
leader and is supported by the
Figure 7-2. CP Evaluation Management Structure
A work breakdown structure (WBS) has been prepared for use in managing all aspects of this earmark evaluation project. The discrete task elements of the project are listed in this work breakdown structure, displayed in Figure 7-3. Battelle’s project manager will refer to this work breakdown structure to track and monitor all tasks and deliverables for the project.
Figure 3.
Work Breakdown Structure
Figure 7-3. CP
Evaluation Work Breakdown Structure
Table 7-1 presents the estimated level of effort for the completion of the evaluation. Note that, per DOT approval, additional efforts were made in performing an exploratory analysis which was not in the original scope. An estimated 270 hours were spent in the planning, execution, and reporting of the exploratory analysis. Additional funding might be needed to complete the remaining tasks of this evaluation.
Table 7-1. Estimated Level of Effort
Task
|
Estimated
Hours
|
|||
Senior Analyst |
Middle Analyst |
Junior Analyst |
Total
|
|
0. Exploratory
Analysis |
90 |
120 |
60 |
270 |
1. Evaluation
Plan |
150 |
20 |
0 |
170 |
2. Test
Plans |
115 |
55 |
0 |
170 |
3. Data
Collection |
140 |
244 |
666 |
1050 |
4. Data
Analysis |
35 |
300 |
100 |
435 |
5. Interim
Briefing |
50 |
0 |
0 |
50 |
6. Evaluation
Report |
161 |
85 |
0 |
246 |
Total |
741 |
824 |
826 |
2391 |
This project will collect large amount of system data in the electronic form. Those data will be acquired and archived using an industry standard format such as SAS® or Microsoft Access. This evaluation will preserve all raw and processed system data as well as the final numeric analysis outputs generated from such data. Mr. Alan Pate of Battelle is the database manager for this evaluation and will perform all data transfer, processing, and archival throughout the entire study. These electronic data will be saved on Battelle’s data server during the evaluation. Daily backup of all data is automatically performed by the Battelle computer system. At the end of the project, all electronic data will be transferred to the USDOT using approved media such as CD ROM discs.
This section contains the exploratory analysis results referenced in the Section 5 Exploratory Analysis.
Table A-1. Number of Protected and Unprotected Trips on
a Given Weekday for Each
TRAX Station |
Bus Route |
Number of Protected Trips |
Number of Unprotected Trips |
Ballpark |
66 |
4 |
4 |
Central Pointe |
30 |
- |
79 |
35 |
5 |
14 |
|
442 |
- |
38 |
|
|
31 |
- |
81 |
37 |
3 |
28 |
|
41 |
4 |
16 |
|
131 |
- |
4 |
|
137 |
- |
7 |
|
Meadowbrook |
15 |
- |
19 |
36 |
3 |
16 |
|
39 |
- |
70 |
|
42 |
3 |
14 |
|
142 |
- |
4 |
|
442 |
- |
19 |
|
|
40 |
- |
70 |
140 |
- |
8 |
|
|
10 |
- |
56 |
12 |
- |
18 |
|
84 |
- |
26 |
|
Fashion Place West |
22 |
- |
58 |
24 |
- |
26 |
|
33 |
3 |
17 |
|
124 |
- |
10 |
|
125 |
- |
9 |
|
222 |
- |
27 |
|
Midvale |
82 |
- |
30 |
85 |
- |
27 |
|
|
25 |
- |
23 |
27 |
- |
16 |
|
88 |
4 |
2 |
|
125 |
- |
9 |
|
222 |
- |
54 |
|
Historic |
24 |
- |
52 |
90 |
6 |
13 |
|
94 |
7 |
12 |
|
124 |
- |
10 |
|
|
12 |
- |
16 |
24 |
- |
52 |
|
33 |
- |
19 |
|
41 |
1 |
20 |
|
46 |
4 |
- |
|
47 |
4 |
5 |
|
124 |
- |
5 |
|
125 |
- |
4 |
|
133 |
- |
4 |
|
143 |
- |
5 |
|
222 |
- |
27 |
|
345 |
4 |
4 |
|
811 |
22 |
1 |
|
816 |
- |
2 |
|
Total |
|
77 |
1150 |
Table A-2. Distribution of Weekday Bus Trips by Time of
Day vs. Frequency of
Bus Route Frequency |
Number of CP Protected Trips |
Number of Non-CP Protected Trips |
Total |
||||||
AM rush |
Mid-day |
PM rush |
Evening |
AM rush |
Mid-day |
PM rush |
Evening |
||
< 25 min |
0 |
0 |
3 |
0 |
126 |
62 |
140 |
0 |
331 |
25-55 min |
12 |
0 |
52 |
0 |
271 |
156 |
221 |
14 |
726 |
> 55 min |
0 |
4 |
4 |
2 |
0 |
64 |
14 |
73 |
161 |
Undefined* |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
7 |
9 |
Total |
12 |
4 |
59 |
2 |
398 |
283 |
375 |
94 |
1227 |
*only 1 trip
departs on a given route during time period, thus frequency cannot be
calculated
Table A-3. CP Messages Generated During November and December
2002
CP Message Type |
Number of CP Messages |
||
November |
December |
Total |
|
Hold |
202 |
316 |
518 |
Missed |
37 |
78 |
115 |
Bad Transmission* |
0 |
770 |
770 |
Discarded** |
0 |
12 |
12 |
Total |
239 |
1176 |
1415 |
* 558 of these
occurred on weekends, even though no trips are CP protected on weekends
**not
generated by CP System until Version 2 was installed on (or about)
Table A-4. November/December CP Messages Generated for
Buses at Each TRAX Station*
TRAX Station |
Number (Percent) of CP Messages |
Number (Percent) of Protected Trips** |
CP Messages as Percent of Protected Trips |
Ballpark |
29 (4.5%) |
164 (5.2%) |
17.7% |
Central Pointe |
44 (6.8%) |
205 (6.5%) |
21.5% |
|
105 (16.3%) |
287 (9.1%) |
36.6% |
Meadowbrook |
10 (1.6%) |
246 (7.8%) |
4.1% |
|
0 (0.0%) |
0 (0.0%) |
-- |
|
0 (0.0%) |
0 (0.0%) |
-- |
Fashion Place West |
18 (2.8%) |
123 (3.9%) |
14.6% |
Midvale |
0 (0.0%) |
0 (0.0%) |
-- |
|
45 (7.0%) |
164 (5.2%) |
27.4% |
Historic |
149 (23.1%) |
533 (16.9%) |
28.0% |
|
245 (38.0%) |
1435 (45.5%) |
17.1% |
Total |
645 (100%) |
3157 (100%) |
20.4% |
* 770
“Bad Transmission” messages not included
**
based on 41 days and Change Period #3 schedule
Table A-5. Late Train Trips During November and December
by TRAX Station
TRAX Station |
Number (Percent) of Late Train Trips |
Number (Percent) of Scheduled Train Trips* |
Late Train Trips as Percent of Total Train Trips |
Ballpark |
265 (8.3%) |
7999 (9.0%) |
3.3 |
Central Pointe |
230 (7.2%) |
7999 (9.0%) |
2.9 |
|
251 (7.9%) |
7999 (9.0%) |
3.1 |
Meadowbrook |
241 (7.7%) |
7999 (9.0%) |
3.0 |
|
238 (7.5%) |
7999 (9.0%) |
3.0 |
|
376 (11.8%) |
7999 (9.0%) |
4.7 |
Fashion Place West |
335 (10.5%) |
7649 (8.6%) |
4.4 |
Midvale |
346 (10.9%) |
8398 (9.4%) |
4.1 |
|
398 (12.5%) |
8348 (9.4%) |
4.8 |
Historic |
357 (11.2%) |
8348 (9.4%) |
4.3 |
|
146 (4.6%) |
8348 (9.4%) |
1.7 |
Total |
3183 (100%) |
89085 (100%) |
3.6 |
*
based on Change Period #3 schedule
Table A-6. Late Train Trips During November and December
by Day of Week
Day of Week |
Number (Percent) of Late Train Trips |
Number (Percent) of Scheduled Train Trips* |
Late Train Trips as Percent of Total Train Trips |
Sunday |
160 (5.0%) |
6705 (7.5%) |
2.4 |
Monday |
582 (18.3%) |
14877 (16.7%) |
3.9 |
Tuesday |
400 (12.6%) |
14877 (16.7%) |
2.7 |
Wednesday |
246 (7.7%) |
11571 (13.0%) |
2.1 |
Thursday |
331 (10.4%) |
11571 (13.0%) |
2.9 |
Friday |
666 (20.9%) |
14877 (16.7%) |
4.5 |
Saturday |
798 (25.1%) |
14607 (16.4%) |
5.5 |
Total |
3183 (100%) |
89085 (100%) |
3.6 |
*
based on Change Period #3 schedule
Table A-7. Late Train Trips During November and December
by Time of Day
Time of Day |
Number (Percent) of Late Train Trips |
Number (Percent) of Scheduled Train Trips* |
Late Train Trips as Percent of Total Train Trips |
AM rush |
606 (19.0%) |
22608 (25.4%) |
2.7 |
Mid-day |
537 (16.9%) |
19877 (22.3%) |
2.7 |
PM rush |
895 (28.1%) |
25585 (28.7%) |
3.5 |
Evening |
1145 (36.0%) |
21015 (23.6%) |
5.4 |
Total |
3183 (100%) |
89085 (100%) |
3.6 |
*
based on Change Period #3 schedule
Table A-8. Distribution of Bus Trips by Time of Day vs.
Frequency of
Bus Route Frequency |
CP Protected (# trips) |
Non-CP Protected (# trips) |
Total |
||||||
AM rush |
Mid-day |
PM rush |
Evening |
AM rush |
Mid-day |
PM rush |
Evening |
||
Ballpark |
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
4 |
- |
- |
- |
- |
- |
- |
- |
4 |
> 55 min |
- |
- |
- |
- |
- |
- |
4 |
- |
4 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
Central Pointe |
|||||||||
< 25 min |
- |
- |
- |
- |
24 |
24 |
28 |
- |
76 |
25-55 min |
- |
- |
5 |
- |
21 |
- |
19 |
2 |
47 |
> 55 min |
- |
- |
- |
- |
- |
12 |
- |
- |
12 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
1 |
1 |
|
|||||||||
< 25 min |
- |
- |
3 |
- |
30 |
22 |
38 |
- |
93 |
25-55 min |
- |
- |
4 |
- |
19 |
8 |
4 |
7 |
42 |
> 55 min |
- |
- |
- |
- |
- |
4 |
- |
4 |
8 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
Meadowbrook |
|||||||||
< 25 min |
- |
- |
- |
- |
26 |
- |
26 |
- |
52 |
25-55 min |
- |
- |
6 |
- |
26 |
16 |
26 |
- |
74 |
> 55 min |
- |
- |
- |
- |
- |
16 |
- |
4 |
20 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
2 |
2 |
|
|||||||||
< 25 min |
- |
- |
- |
- |
26 |
- |
26 |
- |
52 |
25-55 min |
- |
- |
- |
- |
- |
16 |
- |
- |
16 |
> 55 min |
- |
- |
- |
- |
- |
- |
- |
8 |
8 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
2 |
2 |
|
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
- |
- |
- |
- |
35 |
24 |
36 |
- |
95 |
> 55 min |
- |
- |
- |
- |
- |
4 |
- |
- |
4 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
1 |
1 |
Fashion Place West |
|||||||||
< 25 min |
- |
- |
- |
- |
20 |
16 |
22 |
- |
58 |
25-55 min |
- |
- |
3 |
- |
26 |
16 |
24 |
5 |
74 |
> 55 min |
- |
- |
- |
- |
- |
4 |
- |
14 |
18 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
Midvale |
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
- |
- |
- |
- |
19 |
16 |
18 |
- |
53 |
> 55 min |
- |
- |
- |
- |
- |
- |
- |
4 |
4 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
|
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
- |
- |
- |
- |
36 |
20 |
29 |
- |
85 |
> 55 min |
- |
- |
4 |
- |
- |
4 |
5 |
9 |
22 |
Undefined* |
- |
- |
- |
- |
- |
1 |
- |
- |
1 |
Historic |
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
- |
- |
13 |
- |
30 |
16 |
22 |
- |
81 |
> 55 min |
- |
- |
- |
- |
- |
8 |
- |
10 |
18 |
Undefined* |
- |
- |
- |
- |
1 |
- |
- |
- |
1 |
|
|||||||||
< 25 min |
- |
- |
- |
- |
- |
- |
- |
- |
0 |
25-55 min |
8 |
- |
22 |
|
59 |
24 |
42 |
- |
155 |
> 55 min |
- |
4 |
- |
2 |
- |
12 |
5 |
20 |
43 |
Undefined* |
- |
- |
- |
- |
- |
- |
- |
1 |
1 |
Total |
12 |
4 |
60 |
2 |
398 |
283 |
374 |
94 |
1227 |
*only 1 trip departs
on a given route during time period, thus frequency cannot be calculated
Table A-9. Potential Bus Trips that Could Be Used for a
“With vs. Without” Evaluation
TRAX Station |
Potential “Without” Case |
Corresponding CP-Protected Trip(s) |
# CP Msg |
Train Time |
|||||
Bus Route |
Bus Direction |
Bus Time |
Bus Frequency |
Bus Route |
Bus Direction |
Bus Time |
|||
Fashion Place West |
22 |
Inbound |
|
<25 min |
33 |
Southbound |
|
5 |
|
222 |
Southbound |
|
25-55 min* |
||||||
22 |
Inbound |
|
<25 min |
33 |
Southbound |
|
3 |
|
|
222 |
Southbound |
|
25-55 min* |
||||||
222 |
Southbound |
|
25-55 min* |
33 |
Southbound |
|
5 |
|
|
|
31 |
Eastbound |
|
< 25 min* |
37 |
Westbound |
|
7 |
|
31 |
Westbound |
|
< 25 min* |
||||||
31 |
Eastbound |
|
< 25 min |
41 |
Southbound |
|
9 |
|
|
37 |
Westbound |
|
< 25 min |
||||||
31 |
Eastbound |
|
< 25 min* |
37 |
Westbound |
|
8 |
|
|
31 |
Eastbound |
|
< 25 min |
41 |
Southbound |
|
8 |
|
|
31 |
Eastbound |
|
< 25 min |
41 |
Southbound |
|
8 |
|
|
31 |
Westbound |
|
< 25 min |
||||||
Meadowbrook |
39 |
Westbound |
|
< 25 min |
36 |
Westbound |
|
0 |
|
39 |
Westbound |
|
< 25 min |
42 |
Southbound |
|
0 |
|
|
39 |
Westbound |
|
< 25 min |
36 |
Westbound |
|
1 |
|
|
39 |
Westbound |
|
< 25 min |
42 |
Southbound |
|
2 |
|
|
39 |
Westbound |
|
< 25 min |
36 |
Westbound |
|
1 |
|
|
39 |
Westbound |
|
< 25 min |
42 |
Southbound |
|
1 |
|
|
|
27 |
Inbound |
|
> 55 min* |
88 |
Westbound |
|
2 |
|
27 |
Inbound |
|
> 55 min* |
88 |
Westbound |
|
8 |
|
|
27 |
Inbound |
|
> 55 min* |
88 |
Westbound |
|
9 |
|
|
Historic |
24 |
Southbound |
|
25-55 min* |
94 |
Eastbound |
|
11 |
|
90 |
Westbound |
|
25-55 min* |
||||||
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
9 7 |
|
|
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
8 7 |
|
|
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
5 4 |
|
|
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
8 7 |
|
|
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
12 9 |
|
|
24 |
Southbound |
|
25-55 min* |
90 94 |
Westbound Eastbound |
|
11 10 |
|
|
|
24 |
Northbound |
|
25-55 min* |
41 |
Northbound |
|
0 |
|
24 |
Southbound |
|
25-55 min* |
||||||
33 |
Northbound |
|
25-55 min* |
811 |
To |
|
11 |
|
|
47 |
Northbound |
|
25-55 min* |
||||||
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
4 |
|
|
33 |
Northbound |
|
25-55 min* |
811 |
To |
|
3 |
|
|
47 |
Northbound |
|
25-55 min* |
811 |
To |
|
1 |
|
|
33 |
Northbound |
|
25-55 min* |
||||||
33 |
Northbound |
|
25-55 min* |
811 |
To |
|
0 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
2 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
1 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
1 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
5 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
4 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
5 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
5 |
|
|
33 |
Northbound |
|
25-55 min* |
811 |
To |
|
4 |
|
|
33 |
Northbound |
|
25-55 min* |
47 345 811 |
Southbound Outbound To |
|
2 11 2 |
|
|
24 |
Northbound |
|
25-55 min* |
811 |
To |
|
2 |
|
|
33 |
Northbound |
|
25-55 min* |
46 811 |
Southbound To |
|
8 4 |
|
|
24 |
Northbound |
|
25-55 min* |
345 |
Outbound |
|
4 |
|
|
33 |
Northbound |
|
25-55 min* |
46 47 811 |
Southbound Southbound To |
|
10 5 5 |
|
|
24 |
Northbound |
|
25-55 min* |
345 811 |
Outbound To |
|
7 5 |
|
|
33 |
Northbound |
|
25-55 min* |
46 |
Southbound |
|
9 |
|
|
12 |
Northbound |
|
> 55 min |
46 47 |
Southbound Southbound |
|
9 3 |
|
* same
frequency as protected trip
[1] SPSS is the Statistical Package for the Social Sciences, an analytic software tool that reads standard database formats and allows for a full range of statistical procedures to be applied to the data, including frequency distributions and associated statistics, cross-tabulations to examine relationships among variables in the data, and even more sophisticated forms of analyses.