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Page 67
Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Appendix C - TriMet Case Study." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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67 TriMet provides fixed route bus and light rail service to the Portland, Oregon, metropolitan area. The transit system comprises 91 bus routes, 77 of which connect to a 4-route, 44-mile light rail network. Its fleet of 614 buses and 105 rail cars carried over 95 million boarding rides in 2006. In addi- tion, TriMet is contracted to operate the downtown Portland Streetcar. A noteworthy feature of the transit system is Fareless Square, a downtown zone within which travel is free. Preliminary analysis pointed to the following reasons for selecting TriMet as a case study for this Guidebook: • TriMet was an early adopter of APC technology, and lever- aged its APC experience in the design of its AVL system. The agency has a strong reputation in the industry for its innova- tive uses of archived AVL and APC data in market research, performance monitoring, scheduling, and service planning. • TriMet’s market research program is comprehensive, and the agency has pioneered the development of new attitudinal metrics for understanding customer perceptions. Market re- searchers at TriMet have also used ITS data to leverage tradi- tional market research practices, and have linked customer perception and satisfaction research to AVL and APC data. • TriMet has developed its GIS capabilities within a fully in- tegrated enterprise data environment that includes archived data from ITS technologies. There are a number of high-end GIS users employing ITS data in market re- search and planning applications. The research team met with TriMet staff in January 2007. Organizational Structure TriMet’s organizational structure includes five major divisions: • Finance • Capital Projects • Government Affairs • Operations • Communications and Technology The agency’s market research unit is housed in the Communications and Technology (CT) division, as is the in- formation technology (IT) unit. The service planning and scheduling units reside in the Operations division. Over time, there has been a centralization of functions within IT that had previously been maintained in other units throughout the agency, consistent with the development of TriMet’s enter- prise data system. The IT unit is also responsible for develop- ing enterprise applications utilizing archived ITS data, many of which involve GIS. TriMet’s Experience With ITS Technology TriMet’s experience with APC technology dates from a demonstration project in the early 1980s. In the course of this project, a severe economic downturn in the region forced sig- nificant service cuts and the release of the agency’s entire ride checking staff. Thus the APCs offered the only means of re- covering passenger data for internal and NTD reporting. A staff person was assigned responsibility for “fixing the bugs” in the system, including problems with maintenance, trip and stop referencing (which were based on odometer and time clock stamps in pre-AVL days), and balancing block level boardings with alightings. Although considerable success was achieved in overcoming operational problems with the APCs prior to AVL deploy- ment, usable data recovery rates rarely exceeded 25%. With a fleet penetration rate of about 15% in 1990, the APCs were thus barely able to satisfy TriMet’s data needs for NTD reporting. A study was done to validate the accuracy of pas- senger counts and to design a vehicle/APC assignment plan to satisfy NTD sampling requirements (Strathman and Hopper A P P E N D I X C TriMet Case Study

1991). This plan was implemented for several years, but prob- lems often occurred at the garage level in assigning APC- equipped buses to NTD sample blocks. With improvement in the agency’s financial condition in the 1990s, passenger count- ing for NTD reporting was contracted out and manually recovered. At that point the APC buses were redeployed to support service planning information needs. Despite the initial problems, TriMet’s experience with APCs proved valuable in planning its AVL system. The AVL project team was headed by the staff person who had been responsible for NTD reporting and also included the staff per- son who had been responsible for maintaining the APCs. They recognized that AVL’s improved location referencing capabil- ities would, when integrated with APCs, yield much higher rates of successful passenger data recovery. Specifications for the AVL system thus included a capability to produce and store passenger and operating data records at the stop level. Bandwidth limitations prevented transmission of the large volume of stop level data records over the radio system, requiring storage on an on-board computer. The requirement of location-defined data recovery distin- guished the specification of TriMet’s AVL system from other GPS-based systems that were deployed in the 1990s. All AVL systems featured transmission of real time vehicle location status data to dispatchers on a 30-90 second polling cycle. While this information had considerable value in support of operations management, and has since been extended to cus- tomer information applications (e.g., in posting estimated vehicle arrival times at selected stops and on the Web), it was incompatible with applications requiring information about specific locations (e.g., stops, time points, route origins and destinations). A side benefit from on-board storage of AVL data was a re- duction in the cost of APC units. With data storage comput- ers already on board to serve the AVL system, the cost of adding APC units in the late 1990s fell to about $1,000/ vehicle. Thus, TriMet decided to specify APCs in all new bus acquisitions. Presently, about 75% of the bus fleet and 25% of the light rail fleet are APC-equipped. While this level of penetration is higher than what is needed for NTD and inter- nal ridership reporting, it allows staff to analyze operations and evaluate service in ways that are unique in the industry. A final feature of TriMet’s AVL system that distinguished it from others deployed in the 1990s was a control head installed near the vehicle operator. The control head displays the vehi- cle’s status in relation to its schedule (minutes early/late), and a keypad allows the operator to send pre-programmed text mes- sages to the dispatch center (e.g., 74 = “Mechanical Problem— Out of Service”). The text message records are stored for offline analysis. The AVL system was deployed in 1998. A study compared system performance a year after deployment to performance prior to deployment (Strathman et al. 2000). Although there were no changes in operations practices during the study pe- riod, improvements were observed in on-time performance, running times and running time variation. It was hypothe- sized that the improvements resulted from the “instrumenta- tion effects” of the new system associated with operators having better knowledge of their schedule status and dis- patchers having better knowledge of vehicles’ operating status. TriMet’s estimated breakdown of fare revenue in fiscal year 2008 is 39% farebox (cash), 9% pre-paid ticket sales, and 52% flash pass sales. TriMet does not have advanced electronic fare payment technologies, such as magnetic stripe or smart cards. Electronic registering fareboxes have been in use since 1989. The fareboxes are not integrated with the AVL system, and reporting is thus limited to daily cash receipt totals. Ticket vending machines have also been in service since 1986, with reporting limited to time, location, and fare instrument purchased. Most of the ticket vending machines are located at light rail stations. Generally speaking, TriMet’s electronic fare payment technologies are not used for service delivery or market research analysis. The remaining technologies include automated phone sys- tems and the Web. Tracking software has been used to log Web visits since 2002. Information is thus available on Web links providing trip planning services and real time vehicle ar- rival times at stops (“Transit Tracker”). Customers can also report compliments, suggestions, and complaints on the Web. The automated phone system provides trip planning, arrival time, and customer feedback services, all of which are logged. ITS Data Management A central “core” set of data is maintained and managed as a foundation for all TriMet ITS initiatives. The architecture of the agency’s enterprise data system is illustrated in Figure C-1. The diagram reflects the holistic nature of the data environ- ment and the importance of integration of ITS systems and applications. This ensures that the publication and dissemina- tion of data are accurate and consistent across all systems. Base detailed features of the transit system (e.g., routes, stops) are represented and maintained in Definition & Main- tenance tables. This information is used in conjunction with vehicle itineraries, produced by HASTUS scheduling soft- ware, to prepare the published schedule. After the data are validated, the prepared schedules are written to cards that are inserted by vehicle operators in the log in process. Vehicles in service recover and store data records from the AVL and APC systems. The data records (including schedule and AVL-APC operating data) are downloaded at the end of each day at the garages. Post processing is then undertaken by Operations staff to ensure the validity of the recovered data. 68

Figure C-1. TriMet’s enterprise data system.

Post processing involves a number of steps: (1) matching vehicles’ AVL data records to their schedules and the base map of stops and time points associated with the assigned work; (2) event data recorded at locations other than those represented in the base map are assigned to base map loca- tions (examples include instances where a vehicle drops off or picks up passengers between scheduled stops, records for temporary re-routes, the record of a vehicle’s maximum speed between stops, or an event record); (3) screening to identify extreme values, which may indicate a malfunction- ing unit (records with extreme values are retained, but flagged to indicate that the data may be suspect); (4) “zeroing out” passenger loads at the service block level, and proportionately adjusting boarding and alighting data to correspond to the block load adjustment. After post processing, data are forwarded to BDS (Bus Dispatch System) Event History tables. Event history data, ac- cumulating at a rate of about 600,000 records per day, are then ready to be accessed by staff for analysis. Selected data, such as operator-keyed event records related to stop maintenance and repair, automatically generate Service Requests that are then evaluated by staff and (if re- quired) converted into Work Orders for work. The status of a work order is monitored by the Stop and Amenities Work Order Tracking System (SAMW), and the history is retained for future reference. Following the stop maintenance and repair example, the process might also be initiated through the Customer Service Information (CSI) program. In this case the service request originates from a customer Web or phone contact. In addi- tion to directing the information to Service Requests, the cus- tomer contact would also be logged in a CSI table. Data from the Prepared Schedule tables are also linked to several other customer information systems. The Automated Trip Information System (ATIS), supports the trip planning program offered through the Web and by phone. Transit Tracker supports the phone and Web-based program that provides next arrival times. Data are also generated in the Google Transit Feed Spec (GTFS) format and made available for public access for external applications, such as Google Transit. The final operations-related component of the enterprise data system is the Accident Incident Response System (ACID) and tables, which contain data from incident reports. Spatial data are directly maintained in Oracle tables to en- sure consistent integration of ITS data across the enterprise system and to facilitate access for GIS applications. A number of GIS applications have been developed to support desktop mapping and analysis of data from various Oracle tables. For example, the Real Time Bus Mapper is a Web-based applica- tion that displays the current location of buses in service. Other GIS applications have been developed to map data from the Work Order, CSI, Event History, and Definition & Maintenance tables. GIS staff prepare a basemap for systems throughout the agency for import, including HASTUS, the Automated Trip Information System (ATIS), and the Trapeze Paratransit sys- tem. GIS data collection is used to collect location and amenity information for features of the transit system. As the description of post processing activities indicates, units throughout TriMet are directly responsible for main- taining data in Oracle tables within the enterprise data sys- tem. GIS staff adhere to the same IT standards (Oracle, Java, Linux) and the same Systems Engineering approach to appli- cation development. The evolution of the enterprise data system is guided by system integration strategies that include a database design review process and procurement policies for new systems. Service Delivery Monitoring and Analysis Few, if any, transit properties in the U.S. have developed a service delivery analysis and monitoring capability compara- ble to what TriMet has achieved with its archived AVL and APC data. These achievements are a consequence of the fore- thought given to the specification of ITS data recovery and archiving, the high penetration of APC units in the fleet, and the efforts of highly skilled staff analysts. The service delivery monitoring and analysis activities de- scribed in this section can be distinguished between regular performance reporting, which usually occurs quarterly, and ad hoc analysis, which is undertaken to address specific needs or issues. However, it should be emphasized that regular reporting activity and ad hoc analysis have been strategically related in that the content and coverage of performance re- ports have evolved to incorporate what has been learned from ad hoc analysis. Quarterly performance reports are posted on the agency Intranet in both pdf and Excel format. The trip level sum- mary report serves as the cornerstone of quarterly perfor- mance reporting. The report provided detailed information on passenger activity, on-time performance, and vehicle running times for every scheduled trip in the system. Selected infor- mation from the spring 2007 trip performance report for scheduled AM Peak period inbound trips on the Route 14-Hawthorne is shown in Table C-1. Capacity utilization is represented by the average maximum passenger load per trip. Information on the percentage of trips where the maximum passenger load exceeds 80% of a vehicle’s maximum design capacity provides an indicator of both the variance in capac- ity utilization and the extent of excess demand. The scheduled running time for each trip serves as a bench- mark against which actual running times are compared. 70

Traditional scheduling practice has been to set scheduled running times to approximate the median, although stan- dards exceeding the median have become more common. Actual running times in the quarterly trip report are given for the median, 60th, and 80th percentile of the distribution. A noteworthy feature of the running time statistics in the report is that they have been adjusted to remove excess dwell time, using threshold dwell times required to service stops on the route. Dwells exceeding the threshold are interpreted as likely to reflect holding actions associated with maintaining the schedule. Systematic departures from scheduled running times sig- nal a need to add or trim running times from the schedule. When a decision is made that there is a need to adjust the schedule, plots of actual running times are also produced to aid the process. These plots are similar to those described in the CTA case study. Staff commented that three schedule writers can now do the work that used to require six, and that schedule adherence has improved over time despite worsen- ing traffic congestion. Also, the running time reports are revealing opportunities to remove time from schedules, where operator and customer feedback in the past mostly focused on the need to add time. On-time performance information is presented in the last three columns of Table C-1. “Late” is defined as departures that are more than 5 min after the scheduled departure time; “early” is defined as departures that are more than 1 min before the scheduled departure time; and “on time” is defined as departures that are between 1 min early and 5 min late. There is a readily apparent relationship between scheduled running time, actual running time, and on-time performance. For example, when scheduled running is inadequate, the percentage of late departures increases and the percentage of on-time and early departures declines. In the case of the trips covered in Table C-1, scheduled running times are generally greater than the actual median, 60th percentile, and 80th per- centile values, indicating that the schedule provides more than adequate time for operators to complete their trips. Conse- quently, on-time performance is quite high, and the incidence of early departures generally exceeds the incidence of late departures. Although trip report information on mean maximum pas- senger loads indicated that there was generally sufficient capacity, the variance in maximum passenger loads among blocks of trips on routes during peak service periods also sug- gested that deviations from scheduled headways were con- tributing to instances of overloading. This was confirmed by ad hoc statistical analysis of the relationship between headway de- viation (i.e., “bus bunching”) and passenger loads. Figure C-2 illustrates the relationship for AM Peak inbound trips on Route 14-Hawthorne. In this plot, the average load for vehicles main- taining their scheduled headway (0.00 headway delay) is approximately 45 passengers. For vehicles whose actual head- way has grown to two minutes greater than schedule, average loads are about 55 passengers. Conversely, for vehicles whose actual headway has decreased to two minutes less than sched- ule, average loads fall to about 35 passengers. Thus, it was concluded that the solution for managing (leveling) passenger loads lies in improving headway management. Further statistical analysis exploring the root causes of headway deviations identified late departures from garages and route origins as principal contributors to the problem 71 Start Time Avg. Max Load Percent Over Capacity Scheduled Run Time Median Run Time Less Idle 60th% Run Time Less Idle 80th% Run Time Less Idle Percent On Time Percent Early Percent Late 7:02 AM 45 0% 0:36:00 0:31:26 0:32:18 0:33:19 90% 5% 4% 7:06 AM 27 0% 0:36:00 0:35:10 0:35:44 0:36:48 97% 1% 2% 7:10 AM 43 18% 0:37:00 0:36:14 0:36:38 0:38:20 93% 6% 1% 7:13 AM 46 0% 0:38:00 0:36:37 0:37:18 0:37:58 94% 2% 4% 7:16 AM 31 0% 0:38:00 0:35:49 0:36:14 0:37:36 97% 2% 1% 7:19 AM 31 0% 0:38:00 0:35:39 0:36:12 0:37:38 98% 1% 0% 7:24 AM 49 24% 0:38:00 0:36:25 0:37:08 0:38:14 97% 3% 1% 7:29 AM 32 5% 0:39:00 0:35:43 0:36:32 0:37:21 95% 1% 4% 7:34 AM 38 8% 0:39:00 0:36:40 0:37:32 0:39:18 99% 0% 1% 7:38 AM 19 0% 0:39:00 0:34:44 0:35:02 0:36:08 86% 12% 2% 7:43 AM 40 4% 0:39:00 0:36:46 0:37:22 0:38:10 96% 4% 0% 7:47 AM 33 2% 0:39:00 0:35:56 0:36:36 0:37:58 98% 2% 0% 7:52 AM 40 10% 0:39:00 0:35:48 0:36:12 0:37:32 99% 1% 0% 8:04 AM 45 17% 0:39:00 0:35:28 0:35:59 0:37:47 89% 3% 7% 8:10 AM 40 50% 0:39:00 0:33:28 0:34:00 0:35:42 95% 3% 2% 8:17 AM 35 8% 0:38:00 0:36:14 0:36:44 0:38:20 95% 2% 3% 8:26 AM 49 31% 0:38:00 0:33:14 0:34:25 0:35:49 98% 1% 0% 8:33 AM 31 2% 0:38:00 0:35:38 0:36:14 0:37:42 91% 7% 2% 8:44 AM 35 5% 0:36:00 0:35:06 0:35:25 0:37:27 96% 2% 2% 8:53 AM 40 9% 0:36:00 0:34:48 0:35:36 0:37:18 97% 2% 1% 9:04 AM 42 15% 0:36:00 0:37:22 0:37:38 0:38:56 98% 0% 1% Table C-1. Route 14 Hawthorne trip level summary report, spring 2007 (AM peak inbound trips).

(Strathman et al. 2003). Tabulations of AVL data exhibited instances of habitual late departures from origins (following adequate layover) and from garages. Regular reporting of these tabulations to field supervisors and garage managers followed. (It should be emphasized that TriMet supervisors and managers cannot pursue disciplinary action on the basis of evidence from AVL and APC data. Uses of the data are limited to facilitating accepted operations management practices.) For customers, deviations from scheduled headways trans- late into increased waiting time and a greater likelihood of being passed up by an overloaded vehicle. It has long been understood in the transit industry that waiting time at stops and stations is considered by customers to be much more onerous than their line haul time or time spent in accessing stops or final destinations. TriMet staff developed a measure to capture the additional time passengers spend waiting for scheduled service when vehicles’ actual headways deviate from their scheduled head- way. This measure, termed “excess wait,” is reported quarterly by route, direction, and time period. Table C-2 presents sorted values of excess wait time for the spring 2007 signup. In this case the excess wait value in the first row of the table indicates that passengers on outbound PM Peak trips on Route 8-NE 15th Ave. waited three and a half minutes longer for service than they would have had headways been maintained. Headway adherence values are also reported in Table C-2. These values represent the percentage of instances in which actual headways are within 50% of their scheduled values at time points for the corresponding service blocks. Headway adherence and excess wait are generally, but not directly, re- lated. For example, for a given percentage value of headway adherence, excess waiting time tends to be greater for larger than for smaller scheduled headways. It is apparent in Table C-2 that excess wait time and on- time performance are related. In contrast to the high on-time performance reported in Table C-1, the Table C-2 values as- sociated with the worst excess wait situations are much lower. Also, it is apparent that excess waiting and the gap between scheduled and actual headways are the result of lateness. TriMet’s excess wait measure is based on the same headway and boarding data as the CTA’s wait index measure. In addi- tion to AVL data, which are recovered fleet-wide, both meas- ures require extensive recovery of passenger data. At the CTA, these data are recovered from smart and magnetic stripe card usage, while at TriMet the data are recovered by APCs. Both CTA’s wait index and TriMet’s wait time measures are easily understood. However, an advantage of TriMet’s wait time measure is that, given a monetary value of waiting time (e.g., Mohring et al. 1987), one can directly determine the impact on customer welfare from TriMet’s measure. Another benefit from access to comprehensive passenger data at TriMet is evident in the agency’s approach to evaluating the level of service supplied to routes. Analogous to standard practice in evaluations of highway capacity utilization, TriMet calculates passenger volumes in relation to vehicle capacities over varying hour-long time periods. An example is shown in Table C-3, which presents sorted weekday volume/capacity measures for the spring 2007 signup. In this example, the high- est capacity utilization in the system occurred with outbound 72 0 10 20 30 40 50 60 70 80 -8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 8.00 Headway Delay (minutes) Pa ss en ge r Lo ad Figure C-2. Passenger loads and headway delay on Route 14-Hawthorne. (AM peak inbound trips at peak load point)

73 Route, Direction, Time of Day Daily Trips On Time Early Late Scheduled Headway Headway Adherence Excess Wait (min.) 8-NE 15th Ave - Outbound - PM Peak 14.0 45% 3% 52% 8:58 48% 3:27 4-Fessenden - Outbound - PM Peak 10.0 46% 4% 50% 12:37 49% 3:27 15-Belmont - Outbound - PM Peak 20.0 63% 2% 34% 6:14 49% 3:15 8-Jackson Park - Inbound - PM Peak 15.0 57% 1% 42% 8:19 47% 3:10 96-Tualatin/I-5 - Outbound - PM Peak 10.0 60% 0% 40% 10:57 67% 2:47 64-Marquam Hill/Tigard TC - Inbound - AM Peak 5.0 96% 0% 4% 19:50 90% 2:43 4-Division - Outbound - PM Peak 15.0 65% 6% 29% 7:58 54% 2:33 4-Division - Inbound - PM Peak 10.0 67% 4% 29% 12:43 59% 2:23 51-Vista - Inbound - AM Peak 8.4 89% 8% 2% 17:05 91% 2:11 20-Burnside/Stark - Outbound - PM Peak 7.0 66% 6% 27% 15:14 75% 2:07 17-Holgate - Outbound - PM Peak 11.0 59% 7% 33% 11:00 65% 2:07 94-Sherwood/Pacific Hwy Express - Outbound - PM Peak 14.0 73% 0% 26% 9:21 66% 2:05 72-Killingsworth/82nd Ave - Inbound - PM Peak 16.0 73% 7% 20% 7:42 50% 2:02 99-McLoughlin Express - Outbound - PM Peak 8.0 86% 0% 14% 17:09 84% 1:59 17-NW 21st Ave/St Helens Rd - Outbound - PM Peak 8.0 42% 3% 55% 15:00 75% 1:51 15-NW 23rd Ave - Outbound - Midday 31.0 53% 8% 39% 13:27 65% 1:48 4-Division - Inbound - AM Peak 15.0 89% 2% 9% 7:48 59% 1:43 32-Oatfield - Outbound - PM Peak 6.0 74% 4% 22% 24:34 88% 1:43 9-Powell - Outbound - PM Peak 14.0 62% 7% 32% 8:03 64% 1:42 12-Barbur Blvd - Outbound - Night 19.0 64% 3% 33% 20:08 81% 1:39 71-60th Ave/122nd Ave - Inbound - Midday 27.0 82% 5% 14% 16:18 76% 1:39 15-NW 23rd Ave - Outbound - PM Peak 8.0 48% 8% 44% 15:00 70% 1:38 4-Fessenden - Outbound - Night 23.0 71% 5% 24% 19:52 83% 1:34 72-Killingsworth/82nd Ave - Outbound - PM Peak 16.0 80% 5% 15% 7:43 58% 1:34 8-Jackson Park - Outbound - Midday 32.0 71% 3% 27% 13:11 80% 1:33 15-NW 23rd Ave - Outbound - AM Peak 22.0 72% 5% 24% 5:42 49% 1:33 Table C-2. Headway adherence and excess wait time, spring 2007 (sorted by excess wait time). Route Dir. Begin Time End Time Location Trips Hourly Load Load/Seat Capacity Load/Achievable Capacity 8-Jackson Park Out 5:02 AM 6:01 AM SW 3rd betw. Oak & Stark 1 53 123% 95% MAX Blue Line Out 4:10 PM 5:09 PM Pioneer Sq. South MAX Sta. 6 1,470 191% 92% 61-Marq. Hill/Beaverton TC Out 3:42 PM 4:41 PM SW Campus Dr & Terwilliger 1 52 120% 92% 72-Killingsworth/82nd Ave In 12:08 PM 1:07 PM NE 82nd & MAX Overpass 5 256 131% 92% MAX Blue Line Out 4:20 PM 5:19 PM Pioneer Sq. South MAX Sta. 7 1,694 189% 91% 72-Killingsworth/82nd Ave Out 4:18 PM 5:17 PM SE 82nd & Powell 6 303 129% 90% 72-Killingsworth/82nd Ave In 12:18 PM 1:17 PM NE 82nd & MAX Overpass 5 250 128% 89% MAX Blue Line Out 4:38 PM 5:37 PM Pioneer Sq. South MAX Sta. 7 1,650 184% 89% 72-Killingsworth/82nd Ave Out 7:59 AM 8:58 AM SE 82nd & Powell 5 248 127% 89% 72-Killingsworth/82nd Ave Out 4:09 PM 5:08 PM SE 82nd & Powell 7 345 126% 88% 61-Marq. Hill/Beaverton TC Out 4:12 PM 5:11 PM SW Campus Dr & Terwilliger 2 98 114% 88% 88-Hart/198th Ave Out 12:16 PM 1:15 PM Beaverton Transit Center 1 28 100% 88% MAX Blue Line Out 4:47 PM 5:46 PM Pioneer Sq. South MAX Sta. 6 1,395 182% 87% 72-Killingsworth/82nd Ave Out 7:41 AM 8:40 AM SE 82nd & Powell 6 293 125% 87% 72-Killingsworth/82nd Ave In 2:45 PM 3:44 PM NE 82nd & MAX Overpass 8 391 124% 87% 6-M. L. King Jr Blvd Out 2:58 PM 3:57 PM NE Grand & Pacific 3 146 117% 87% 15-Belmont Out 6:20 PM 7:19 PM SW Salmon & 5th 4 194 125% 87% 72-Killingsworth/82nd Ave Out 7:49 AM 8:48 AM SE 82nd & Powell 6 291 125% 87% 8-Jackson Park Out 5:17 AM 6:16 AM SW 3rd betw. Oak & Stark 2 97 118% 86% 4-Division Out 2:13 PM 3:12 PM SE Division & 12th 4 193 121% 86% 72-Killingsworth/82nd Ave Out 7:05 PM 8:04 PM SE 82nd & Powell 4 193 124% 86% Table C-3. Weekday peak hour capacity, spring 2007 (sorted by maximum achievable capacity utilization).

service on Route 8-Jackson Park at SW 3rd Ave. between Oak and Stark St. during the hour period beginning at 5:02 AM. A single scheduled trip provided service during the period and its average hourly load was 53 passengers. This load was 123% of the seat capacity of service supplied and 95% of the maximum achievable vehicle capacity. Examining service utilization over blocks of trips gives service planners a better sense of when and where additional capacity is needed. Probably the greatest utility from this ap- proach comes in evaluating capacity utilization in corridors served by multiple routes. Here, passenger loads and vehicle capacities can be easily aggregated over routes and trips in the corridor, providing a capacity utilization measure that is more consistent with what customers see and with the per- ceptions of crowding they report in satisfaction surveys. The extensive deployment of APCs also allows TriMet to produce a stop level passenger census on a twice-yearly cycle. An example of passenger census information is shown in Table C-4 for inbound stops on a section of Route 8-NE 15th Ave. in weekday service. Average daily boarding and alighting statistics are reported, as are monthly lift deployments (from AVL vehicle monitoring data records). Passenger census in- formation is also organized and reported by stop, summariz- ing passenger movements across all routes serving given stops. Passenger census information is widely used throughout the agency. Within Operations the information is used primarily for stop planning, determining where amenities should be added, and identifying when and where customers with disabilities are connecting with the system. A recent large scale planning effort that focused on stop placement and stop consolidation also drew heavily on passenger census informa- tion. Passenger census information is also important in market research, as will be further discussed in the following section. Conducting the passenger census using APC data has a number of advantages over the manual recording process that had been previously used. First, in the present system, data are current in comparison to the manual approach, which operated on a five-year cycle. Second, in the present system, data are comprehensive and precise in comparison to the old system, where each stop was sampled once in a six- month data collection period. Third, the new system replaces a manual process that was expensive, costing about $250,000 when it was last done in the 1990s. Operator-keyed event data are a key feature of TriMet’s AVL system, with 51 preprogrammed messages that operators can transmit to dispatchers by pressing selected numbers on the vehicle control head. Events include incidents and cir- cumstances that are directly or indirectly related to customers’ riding experience. Table C-5 shows a frequency breakdown for 16 event types recorded between June 3 and July 22, 2007. As previously discussed, events requiring maintenance or 74 Route No. Route Description Direction Stop Location Ons Offs Total Monthly Lifts 8 8-NE 15th Ave 1 NE 15th & Killingsworth 93 22 115 23 8 8-NE 15th Ave 1 NE 15th & Sumner 37 5 42 1 8 8-NE 15th Ave 1 NE 15th & Alberta 254 105 359 32 8 8-NE 15th Ave 1 NE 15th & Going 50 8 58 15 8 8-NE 15th Ave 1 NE 15th & Prescott 68 11 79 3 8 8-NE 15th Ave 1 NE 15th & Mason 40 13 53 1 8 8-NE 15th Ave 1 NE 15th & Failing 31 9 40 2 8 8-NE 15th Ave 1 NE 15th & Fremont 106 49 155 21 8 8-NE 15th Ave 1 NE 15th & Siskiyou 21 6 27 0 8 8-NE 15th Ave 1 NE 15th & Knott 40 8 48 7 8 8-NE 15th Ave 1 NE 15th & Brazee 25 11 36 0 8 8-NE 15th Ave 1 NE 15th & Thompson 17 3 20 0 8 8-NE 15th Ave 1 NE 15th & Tillamook 34 20 54 29 8 8-NE 15th Ave 1 NE 15th & Weidler 48 97 145 15 8 8-NE 15th Ave 1 NE 15th & Halsey 27 52 79 7 8 8-NE 15th Ave 1 NE 16th & Multnomah 17 18 35 1 8 8-NE 15th Ave 1 NE Multnomah & 13th 122 178 300 20 8 8-NE 15th Ave 1 NE Multnomah & 11th 97 49 146 8 8 8-NE 15th Ave 1 NE Multnomah & 9th 80 29 109 16 8 8-NE 15th Ave 1 NE Multnomah & 7th 28 24 52 23 8 8-NE 15th Ave 1 NE Multnomah & Grand 24 39 63 6 8 8-NE 15th Ave 1 NE Multnomah & 3rd 22 13 35 3 8 8-NE 15th Ave 1 Rose Quarter Transit Center 241 169 410 68 8 8-NE 15th Ave 1 NW 3rd & Flanders 99 78 177 16 8 8-NE 15th Ave 1 NW 3rd & Couch 125 117 242 31 8 8-NE 15th Ave 1 SW 3rd between Oak & Stark 14 204 218 44 8 8-NE 15th Ave 1 SW 3rd & Yamhill 2 9 11 3 8 8-NE 15th Ave 1 SW 3rd & Madison 1 9 10 1 Table C-4. TriMet passenger census, spring 2007 signup (weekday average).

repair are automatically forwarded as work requests, ensuring a more timely response to these problems. Given that the event messages are operator initiated, it is likely that event counts understate the actual incidence of most of the phenomena that are covered in event records. Nevertheless, the event data provide important information in support of customer service and operations functions. A customer complaint about being passed up, for example, can be more readily resolved when event records show that the vehicle had been instructed to skip stops or was overloaded with passengers. The ability to document such facets of ser- vice delivery, along with the security enhancing features of the AVL system, help to explain why many operators favor- ably view the addition of this and other technologies. Fare evasions are easily the most common operator-keyed events. Analysis of these event records can reveal patterns in time and space that can inform fare payment enforcement. With information such as that portrayed in Figure C-3, supervisors can assign fare inspectors to the “hot spots” where fare evasion is concentrated. While the map in Figure C-3 por- trays fare evasion frequency at the traffic analysis zone level, the frequencies can also be portrayed at the stop level at finer geographic resolution. Beyond facilitating the regular internal service delivery monitoring functions at TriMet, AVL and APC data also con- tribute to external reporting requirements and ad hoc evalu- ations. TriMet now samples the AVL-APC data archive for NTD reporting for bus and light rail service. The data valida- tion and sampling procedures for the respective modes are described in Kimpel et al. (2003) and Strathman et al. (2005). Archived AVL data were used to evaluate the accuracy and precision of the real time vehicle arrival information pro- duced by the agency’s Transit Tracker system (Crout 2007). Changes in running time and running time variation have been evaluated following the implementation of signal prior- ity technology at selected intersections in the city of Portland (Kimpel et al. 2005). And changes in passenger activity, run- ning time, and running time variation have been evaluated following the relocation and consolidation of stops on se- lected bus routes (El-Geneidy et al. 2006). Use of ITS Data in Market Research TriMet maintains a comprehensive market research pro- gram. The most recent systemwide on-board survey of origins and destinations was completed in 2000. Since the 2000 sur- vey, O-D data has been updated periodically on routes where major service changes have occurred. An annual attitude and awareness survey of riders and non-riders in the Portland re- gion is conducted by telephone. Regional telephone surveys of customer satisfaction are also conducted periodically. Lastly, TriMet conducts an annual on-board survey to determine the mix and usage of fare instruments. The data from this survey are used to understand average pass use rates, employer pass usage, and who uses which method of fare payment. TriMet’s integration of ITS data with advanced market re- search practice is best illustrated in two research projects. The first used surveyed customer satisfaction data to construct satisfaction impact scores for twenty-seven service attributes. Customer perceptions of service attributes were then selec- tively compared to “reality” (represented by ITS data) to identify feasible improvements the agency could make that would have the largest customer satisfaction benefit. The sec- ond project drew on surveyed attitudinal data from riders and non-riders in the Portland region in a market segmenta- tion analysis that sought to identify improvements that would have the greatest likelihood of retaining existing riders and attracting new riders. TriMet’s research on service attribute satisfaction is based on impact scores constructed from two pieces of customer survey information. The first piece is provided by the response to a question asking whether the customer had a problem related to a given attribute on his/her most recent trip. Those customers who respond “yes” are placed in one group and those who respond “no” are placed in a second group. The second piece of information is the customer’s overall satisfaction rating (on a ten-point scale) of that at- tribute. For each attribute, a satisfaction impact score is cal- culated as the difference in mean satisfaction between the “yes” and “no” groups multiplied by the percentage of re- spondents in the “yes” group. Thus a high satisfaction impact score could be the result of a small percentage of customers experiencing a problem that has a large effect on their overall satisfaction (e.g., safety-related attributes) or, at the other extreme, a large percentage of customers experiencing a 75 Event Type Number of Events Fare evasion 19269 Passup-overload 827 Mechanical problem-out of service 688 Skip stopping per dispatch 439 Route blocked 364 Lost/found item 349 Emergency-police 294 Sign/pole problem @ stop 282 Litter problem 231 Verbal passenger 225 Lift-rolling/passup 142 Shelter glass broken 114 No-injury accident 106 Emergency-medical 99 Shelter graffiti 92 Report hazard 76 Table C-5. Customer-oriented operator-keyed events, June–July 2007.

Figure C-3. Geographic incidence of operator-keyed fare evasion events, June 2007. (Supervisor District 3)

problem that has a modest effect on their satisfaction (e.g., availability of seats on a vehicle). The twenty-six attributes included in the customer satisfaction survey are presented in Figure C-4. The attributes can be grouped into categories re- lated to service delivery, information provision, comfort, amenities, safety, and fare payment. Among bus riders, TriMet found the satisfaction impact scores for twelve of the twenty-six attributes were statistically significant (see Figure C-5). The two largest satisfaction impact scores (and four of the top twelve) are associated with service delivery. Four comfort-related attributes are also among the top twelve, followed by three customer information-related attributes. The impact scores help to identify the attributes that de- tract from customers’ travel experience and can be expected to influence their future travel choices. While some factors (e.g., odors) are beyond a transit provider’s control, most are not. With limited resources, managers cannot respond to all of the attributes that have an important influence on cus- tomer satisfaction, but the impact scores do help to prioritize actions. ITS data can facilitate this process in two ways. First, the data can help to identify where given problems are most acute. For example, combining schedule adherence data from AVL with APC passenger load data, one can ask, “Where are reliability problems affecting the greatest number of cus- tomers?” Also, using the same data, one can ask, “Is our worst overcrowding due to limited capacity or to poorly main- tained headways?” Or, with AVL schedule adherence and APC boarding and alighting data, one can ask “What are our highest volume/longest wait stops lacking shelters?” In short, 77 Service Delivery • Frequency/short wait times • Reliable service/on schedule • Vehicle not overcrowded • Courteous/quick drivers • Driver assistance/special needs • Adequate capacity at park & ride lots Information Provision • Availability of real time information • Delays explained/announced • Clearly marked/visible stops • Clear/timely announcements • Availability of schedule information at stops • Availability of schedules/maps Comfort • Absence of offensive odors • Smoothness of rides/stops • Physical condition of the vehicle • Availability of seats on vehicle • Comfort of seats on vehicle • Cleanliness of vehicle exterior • Cleanliness of vehicle interior • Cleanliness of stops/stations • Freedom from nuisance behavior Amenities • Availability of shelters Safety • Safety from crime at stops • Safety from crime on vehicle Fare Payment • Affordability of trip • Ease of paying fares Figure C-4. Attributes included in TriMet’s customer satisfaction impact analysis. Bus Impact Scores March 2004 5 0 10 15 20 25 30 Fr eq ue nc y Re lia ble se rvi ce /on sc he du le Ab se nc e o ffe ns ive od or s Sm oo thn es s of rid es /st op s Av ail ab ilit y o f s he lte r a t s to ps Ve hic le no t o ve rcr ow de d Av ail ab ilit y o f s ea ts on ve hic le Co urt eo us /qu ick dr ive rs Av ail ab ilit y o f re al- tim e i nfo Co mf ort of se ats on ve hic le Ex pla in/ an no un ce de lay s Cle arl y m ark ed /vi sib le sto ps Im pa ct S co re Service Comfort Amenities Information Figure C-5. Satisfaction impact scores from TriMet customer survey.

ITS data can help managers achieve the greatest improvement in satisfaction with the limited resources at their disposal. Second, ITS data can supplement customer satisfaction analysis over time through ongoing monitoring of service delivery, as well as by linking to replications of customer sat- isfaction surveys. For example, an earlier satisfaction impact study was completed in 2001, and the attributes with the five highest scores were, in order • Frequency, • Availability of a seat, • Explain/announce delays, • Reliable service/on schedule, and • Availability of schedules and maps. Comparing the 2001 and 2004 findings, it appears that re- liability is an increasingly important satisfaction issue for cus- tomers. How does the change in satisfaction correspond to on-time performance trends over the period? Has service reliability deteriorated or has reliability become increasingly important to customers? Further analysis of ITS data allowed TriMet market re- searchers to gain a better understanding of the linkages between service delivery trends and customer satisfaction. For example, while the trend in on-time performance over the period was essentially flat, examination of AVL headway data revealed that the incidence of bus bunching had in- creased. Bunching can affect customer perceptions in two ways. First, they are likely to view bus bunching as a reliabil- ity problem, independent of what on-time performance sta- tistics say. Second, bus bunching removes effective capacity from a route and thus leads to an increase in the incidence of overcrowding, which was also observed in the 2004 study but not in 2001. Thus, in this instance, the resolution of two customer satisfaction problems depended on implementing operations control measures to better manage bus headways. The ability to identify and respond to distinct travel market segments has been found to be a common feature of transit agencies that have built high ridership systems (TranSystems et al. 2007). These agencies strategically target marketing and cus- tomer information, fare options, and service improvements, ensuring that resources are invested where they will produce maximum ridership returns. ITS data offers a cost-effective, timely, and comprehensive means of monitoring customer re- sponses to targeted marketing investments. Market segmentation research in the transit industry is evolving from the days when travelers were distinguished by age, sex, income and car ownership to more sophisticated approaches that supplement the traditional metrics with in- formation on attitudes and preferences. An example of the new approach is a market segmentation study of TriMet’s service area by Gilmore Research Group, first conducted in 1993, and updated in 1998 and 2004. In the 2004 study, a population survey was conducted by telephone with over 2,600 service area residents. Along with demographic and travel activity information, the survey recovered scaled re- sponses to a variety of questions related to travel preferences, the benefits associated with transit as a travel option, TriMet’s performance as the region’s transit provider, and general conditions in the region. The first step in the analysis of the survey data employed a factor analysis of scaled responses to the battery of attitudinal questions. The factor analysis identified five underlying principal components organized around the responses to eighteen of the survey’s attitudinal questions. Next, cluster analysis was performed on the eighteen attitudinal question responses. This analysis yielded the five cluster groups, or market segments, presented in Figure C-6. Several things should be noted about the characteristics associated with these market segments. First, with respect to travel mode, re- spondents were defined to be transit riders if they reported having taken more than two trips on the system in the past month. Second, while the clusters were generated from attitudinal data, distinctions between segments related to in- come, demography, and length of residence sometimes emerged. The largest market segment cluster, labeled “Transit is a Lifestyle Choice,” represents over one-third of the survey re- spondents, 43% of transit riders, and 28% of non-riders. Compared to other segments, members of this group tend to be more supportive of transit (particularly bus) as a travel option and hold more favorable opinions about TriMet’s performance as a transit provider. They view transit as a convenient and economical means of travel, and recognize transit’s social and environmental contributions to the region’s livability. They tend to be newer to the Portland area, better educated, and more likely to reside in urban neighborhoods. Given these defining characteristics, TriMet market researchers concluded that the strategy with the greatest likelihood of retaining current transit riders and attracting non-riders from this segment should focus on improving service. The second largest market segment is labeled “I’m not Comfortable Riding the Bus.” This group includes over one- quarter of the sample and more than one-fifth of the transit riders surveyed. Its disproportionately female members ac- knowledge transit’s benefits, but also express discomfort with being around strangers and concern about their personal safety. Retention of existing riders will likely depend on pro- viding enhanced security measures, and making other changes that would reduce their waiting time at stops and make their waits more amenable. Attraction of non-riders will further depend on effectively communicating the improvements that are made. 78

If there is such a person as the “median citizen” in the Port- land region, he/she would be a member of the third largest market segment, labeled “I Use Transit When it Makes Sense.” The demographics of this group are close to the re- gional averages. They are not disinclined to use transit, but neither are they so inclined. Promoting the advantages of transit use coupled with service improvements likely offers the greatest prospect of providing this group a compelling reason to choose transit over other modes. The fourth largest market segment, labeled “There’s no Way I’m Getting on a Bus,” associates transit with everything they see going wrong in the Portland region, and there is likely to be little that can be done that would sway members of this cluster from that opinion. They are relatively well off, have lived in the area the longest, and are convinced that auto is the only way for them to travel. The smallest market segment, labeled “Riding the Bus Saves Money for My Family,” views transit as the most eco- nomical (and, for many, the only) option for traveling in the region. Improvements in comfort and speed are changes that would provide the most likely means of building ridership in this segment. Defining market segments provided TriMet with a basis for developing strategies to grow transit ridership in the Portland region in a cost effective way. The actual implementation of strategies tailored to each market segment also depended on knowing where each market segment was geographically con- centrated. Sampled market segments were inferred to zip code populations and mapped in relation to the transit sys- tem. Figure C-7 shows where the “Transit is a Lifestyle Choice” market segment is concentrated. Service improve- ments that target this group should be directed to routes serv- ing areas where the group’s concentration is highest. The effectiveness of the service improvements can be moni- tored by relating APC boarding and alighting data from stops in the subject areas to overall passenger trends. It would not be surprising to observe ridership growing faster where the service improvements are implemented. Rather, the effectiveness of the market segmentation analysis should be based on the extent to which the observed rate of ridership increase in the target areas exceeds a benchmark rate representing the average system level ridership response to a change in the level of service. In addition to the two research projects reviewed above, market research staff at TriMet draw on ITS data to facilitate their ongoing practices. APC ridership data provide the great- est utility, and serve in the following applications: • Stop level boardings and alightings are used for on-street intercept surveys to determine the best times to survey, and when or where more than one surveyor is needed. • Route level ridership data are used for on-board intercept surveys to determine sampling rates, to determine how many surveys to print, and to determine weighting factors for expanding sample responses to population totals. • Ridership data are analyzed to assess the effects of changes to the system, including fare increases and service hours. • Survey responses on satisfaction with “overcrowding” are compared with passenger load data to identify specific 79 Market Segment Label Composition Demographic-Attitudinal Characteristics Attraction-Retention Strategy “Transit is a Lifestyle Choice” 43% of Riders 28% of Non-riders 35% of Sample • Pro-bus & pro-TriMet • See riding as convenient, economical, & good for Portland’s livability • Newer to the area, well educated, & live in urban neighborhoods • Likely to respond to service improvements “I Use Transit When it Makes Sense” 18% of Riders 14% of Non-riders 16% of Sample • Demographics similar to the region’s • No strong attitudinal barriers toward using transit • Don’t have a compelling reason to use transit more often • Likely to respond to promotional marketing & service improvements “Riding the Bus Saves Money for My Family” 10% of Riders 10% of Non-riders 10% of Sample • Predominantly male & more ethnically diverse • More children at home • Transit is “a way to get around” • Places low value on transit’s environmental & social benefits • Likely to respond to service improvements, new service, & more stop amenities “I’m not Comfortable Riding the Bus” 22% of Riders 29% of Non-riders 26% of Sample • Predominantly female • Not comfortable around strangers • Concerned about personal safety • Recognizes transit’s environmental & social benefits • Likely to respond to security measures, more stop amenities, and service improvements that reduce wait time “There’s no Way I’m Getting on a Bus!” 7% of Riders 19% of Non-riders 13% of Sample • Married, homeowner, high income, longer term resident • Anti-transit & anti-TriMet • If they ever ride, only use light rail • Always prefer to drive, even in rush hour traffic • None Figure C-6. Portland region’s transit market segments in 2004.

circumstances where high passenger loads are affecting customer satisfaction. • Survey vendors draw on stop and route passenger data to gain a better understanding of the dynamics of the system’s operations. In one instance, APC data have substituted for traditional practice. Prior to 2004, monthly ridership estimates were deter- mined from an annual passenger fare survey, which provided the mix and usage of fare instruments, along with monthly fare rev- enue receipts by type of fare (e.g., cash, pass). Changes in fares and fare instruments were increasingly complicating the esti- mates, and the traditional approach was replaced by a sampling and estimation procedure that drew on archived APC data. Web and phone data are used by market research staff in assessing the effectiveness of communications with customers and in evaluating customer communication with TriMet. Page hits on the Web proxy the visibility of messages to cus- tomers. With a major reconstruction of the downtown transit mall currently underway, a low number of hits on the con- struction and service information links would indicate that more promotion would be needed to increase the visibility of this information. Market research staff have conducted Web satisfaction surveys. Pages or links with high hits that draw low survey sat- isfaction responses would be prime candidates for redesign. The logs from the Trip Planner and Transit Tracker links could be compared to demographic data from the origin- destination survey to identify where communication gaps exist between the agency and customer groups. These gaps could be targeted for additional promotion of available in- formation technology options or could be designated as areas that need traditional printed materials. Issues, Observations, and Challenges There is substantial evidence of a progressive evolution in TriMet’s 10+ years experience with the recovery, archiving, and analysis of ITS data. It is noteworthy that when TriMet’s AVL system was deployed, vendor-developed reporting soft- ware did not exist. Staff developed the reports described in this case study from structured queries of the event history tables. However, as the other case studies have demonstrated, such data query skills proved essential in capturing the full value of ITS data. 80 F11174-AppC-007.eps Figure C-7. Spatial distribution of the “Transit is a Lifestyle Choice” cluster group.

In realizing value from ITS data, TriMet has benefited from staff resources that compare favorably to those at the CTA and Madison Metro Transit. An institution-level employment measure does not necessarily reflect IT and research staff levels; it does offer one explanation for why TriMet has expe- rienced success in utilizing AVL and APC data for market research and planning while other transit properties have often struggled to advance beyond standardized reports. In the course of our interviews, we asked TriMet staff to re- flect on issues, challenges, and lessons learned from working with ITS data. The case study concludes with a summary of staff observations. Market Research • Market Research staff operate in a statistical package for the social sciences (SPSS) environment. Data cannot be weighted within GIS for expansion, so selected fields have to be first ex- ported to Excel, manually weighted, and then imported into a GIS. The process is time consuming and cumbersome. • Market Research staff use SPSS; Operations staff use SAS; and the ITS data are in Oracle tables. The software envi- ronment supporting data analysis is not very user friendly. • Metadata describing the contents of the ITS data archive are lacking. There are only a few people who fully under- stand the data archive. Understanding and finding the exact data one needs requires multiple requests, is time consuming and uses two people’s time instead of one. • Survey data could be geocoded and placed in the data archive so that it would link to ITS data and be available for agency-wide use. However, there is a desire in market re- search to maintain control over the availability of survey data to avoid situations where the data are misunderstood or misinterpreted. Operations/Service Planning/Scheduling • Errors in data or interpretation can ruin trust. • It has been a challenge aligning information from ITS data with what top management needs to make better decisions. • When managers are used to such detailed information, the time and effort that it takes to create, maintain, and ana- lyze the data begins to lose value. • Data matching is essential; AVL and APC data are virtually useless if they cannot be matched to the schedules. ITS ven- dors have very limited capabilities in this area. The data matching process is not simple; it must be fully automated to handle the large volume of data records and it must provide sufficient feedback to allow “malfunctions” in the data to be detected. The process must be actively monitored because there are always new twists in how vehicles are operated, including reroutes, operations control actions, and operator misbehavior. TriMet’s data matching practices began prior to the deployment of AVL, when odometer and time-stamped APC data were matched to schedules, and have evolved to the current heuristic programs that incorporate a variety of issues discovered over time. Fundamentally, successful data match- ing requires active collaboration between programmers and staff familiar with scheduling data practices. • Post processing of the raw data is an important step; there are a number of anomalies in the raw data. But keep the raw data because some applications need it. • Staff responsible for post processing the AVL, APC and event data have been a valuable resource to other staff in their efforts to analyze ITS data. • Be sure to get detailed documentation from vendors. For example, staff was told that a data record was created when the bus door opens, but discovered 10 years later that a record is created when the bus enters the GPS stop circle. • Allowing unlimited access to ITS microdata can lead to misuse and misinterpretation. This advocates for granting wide access to summary data and restricted access to indi- vidual data records. Information Technology and GIS • The TriMet system generates 36 million data records every three months. The data are complex in that both spatial and temporal features must relate to each other. A rela- tional database management system (RDBMS) provides the only way to manage this data in an effective way so staff can answer complex questions that cross department boundaries. • There is a tendency to have a single-minded focus in spec- ifying and deploying ITS technologies in transit. It is im- portant to engage IT staff early in the process to ensure compatibility with the enterprise data model and to allow for applications beyond the designed use of the technology. • Metadata for the data model and data tables is usually not created because it is very time intensive. Consequently, most analysts don’t fully know what data are available in the en- terprise system and cannot take full advantage of it. A good technical writer is needed to create data documentation. • With emerging applications like Google Transit, it is be- coming increasingly important for transit data to be publicly accessible in a standard format. Without an Enterprise Data Environment, this can be difficult and complicated. • Larger transit properties need a database administrator (DBA) to manage the enterprise data system. Smaller prop- erties also have enterprise data system management needs, which could potentially be met by contracting out or pool- ing resources among properties to hire a single DBA. • An enterprise RDBMS is needed to fully support GIS ap- plications using ITS data. 81

References Crout, D.T. Accuracy and Precision of TriMet’s Transit Tracker System. Presented at the 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007. El-Geneidy, A.M., Strathman, J.G., Kimpel, T.J., and Crout, D.T. The Effects of Bus Stop Consolidation on Passenger Activity and Transit Operations. In Transportation Research Record: Journal of the Transportation Research Board, No. 1971, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 32–41. Gilmore Research Group. 2004 Market Segmentation Study. Final report prepared for TriMet, Portland, OR, 2004. Kimpel, T.J., Strathman, J.G., Bertini, R.L., and Callas, S. Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1925, Transportation Re- search Board of the National Academies, Washington, D.C., 2005, pp. 156–166. Kimpel, T.J., Strathman, J.G., Callas, S., Griffin, D. and Gerhart, R.L. Automatic Passenger Counter Evaluation: Implications for National Transit Database Reporting. In Transportation Research Record: Journal of the Transportation Research Board, No. 1835, Transportation Research Board of the National Academies, Wash- ington, D.C., 2003, pp. 93–100. Mohring, H., Schroeter, J., and Wiboonchutikula, P. The Values of Waiting Time, Travel Time, and a Seat on the Bus. Rand Journal of Economics, Vol.18, No. 1, 1987, pp. 40–56. Strathman, J.G., Dueker, K.J., Kimpel, T.J., Gerhart, R.L., Turner, K., Taylor, P. Callas, S., and Griffin, D. Service Reliability Impacts of Computer-Aided Dispatching and Automatic Vehicle Location Technology: A TriMet Case Study. Transportation Quarterly, Vol. 54, No. 3, 2000, pp. 85–102. Strathman, J.G., Kimpel, T.J., and Callas, S. Rail APC Validation and Sampling for NTD and Internal Reporting at TriMet. In Trans- portation Research Record: Journal of the Transportation Research Board, No. 1927, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 217–222. Strathman, J.G., Kimpel, T.J., and Callas, S. Headway Deviation Effects on Bus Passenger Loads: Analysis of TriMet’s Archived AVL-APC Data. Center for Urban Studies, Portland State University, Port- land, OR, 2003. Strathman, J.G., and Hopper, J.L. An Evaluation of Automatic Passen- ger Counters: Validation, Sampling, and Statistical Inference. In Transportation Research Record 1308, TRB, National Research Council, Washington, D.C., 1991, pp. 69–77. TranSystems, Planners Collaborative, Inc., and Tom Crikelair Associ- ates. TCRP Report 111: Elements Needed to Create High-Ridership Transit Systems. Transportation Research Board, National Acade- mies, Washington, D.C., 2007. TriMet Staff Interviewed Doug Allen, Computer Technology Specialist Brett Baylor, Manager, Oracle Technology Terry Bryll, Systems Analyst Programmer IV Steve Callas, Manager, Service and Performance Analysis David Crout, Planner/Analyst Rex Fisher, Schedule Data Technician James Hergert, Manager, Service Planning Nancy Jarigese, Senior Financial Analyst Bibiana McHugh, IT GIS and Location Based Services Tim McHugh, Chief Technology Officer Ginger Shank, Senior Research Analyst Tom Strader, Senior Research Analyst 82

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TRB's Transit Cooperative Research Program (TCRP) Report 126: Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook examines intelligent transportation systems (ITS) and Transit ITS technologies currently in use, explores their potential to provide market research data, and presents methods for collecting and analyzing these data. The guidebook also highlights three case studies that illustrate how ITS data have been used to improve market research practices.

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