Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook (2008) / Chapter Skim
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Appendix C - TriMet Case Study
Appendix C - TriMet Case Study
Pages 67-82
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From page 67... ...
Market researchers at TriMet have also used ITS data to leverage traditional 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 integrated enterprise data environment that includes archived data from ITS technologies.
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From page 68... ...
They recognized that AVL's improved location referencing capabilities 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.
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From page 69... ...
Figure C-1. TriMet's enterprise data system.
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From page 70... ...
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 capacity utilization and the extent of excess demand. The scheduled running time for each trip serves as a benchmark against which actual running times are compared.
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From page 71... ...
Systematic departures from scheduled running times signal 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.
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From page 72... ...
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 ontime performance are related.
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From page 73... ...
Headway adherence and excess wait time, spring 2007 (sorted by excess wait time)
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From page 74... ...
Probably the greatest utility from this approach 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 perceptions of crowding they report in satisfaction surveys.
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From page 75... ...
TriMet's integration of ITS data with advanced market research 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.
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From page 76... ...
Figure C-3. Geographic incidence of operator-keyed fare evasion events, June 2007.
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From page 77... ...
" 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.
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From page 78... ...
Further analysis of ITS data allowed TriMet market researchers 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 increased.
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From page 79... ...
• 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.
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From page 80... ...
In one instance, APC data have substituted for traditional practice. Prior to 2004, monthly ridership estimates were determined from an annual passenger fare survey, which provided the mix and usage of fare instruments, along with monthly fare revenue receipts by type of fare (e.g., cash, pass)
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From page 81... ...
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.
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From page 82... ...
The Effects of Bus Stop Consolidation on Passenger Activity and Transit Operations. In Transportation Research Record: Journal of the Transportation Research Board, No.
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