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Using Archived Data to Generate Transit Performance Measures

Using Archived Data to Generate Transit Performance Measures. 82nd Annual Meeting Transportation Research Board January 13, 2003. Robert L. Bertini Department of Civil & Environmental Engineering Ahmed El-Geneidy School of Urban Studies and Planning Portland State University.

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Using Archived Data to Generate Transit Performance Measures

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  1. Using Archived Data to Generate Transit Performance Measures 82nd Annual Meeting Transportation Research Board January 13, 2003 Robert L. Bertini Department of Civil & Environmental Engineering Ahmed El-Geneidy School of Urban Studies and Planning Portland State University

  2. Problem Statement • Importance of transit service • New ITS monitoring and management systems • Performance monitoring—real time & in retrospect • Past • Limited scope and duration • Aggregate measures • Costly data collection • Now • Unlimited coverage and continuous duration • Design, extract and test specific measures • Actual system performance • Data management/processing challenges • Need for generating relevant measures

  3. Objectives • Describe how archived dispatch system database can be used to generate performance measures. • Improve service standards and effectiveness. • Begin process for developing, testing, using and incorporating performance measures into daily operations. • Focus on experimental set (pilot) of measures. • Part of larger transit operations research program under Great Cities’ Universities Coalition and partially funded by Trimet.

  4. Framework Service Inputs Labor, Capital, Fuel Cost Effectiveness Cost Efficiency Service Outputs Veh-Hrs, Veh-Miles Service Consumption Pax, Pax-Miles, Revenue Service Effectiveness

  5. Performance Measures • Measuring system performance is the first step toward efficient and proactive management. • Increasing attention to transit performance • Transit Capacity and Quality of Service Manual • Quantitative/qualitative • Passenger point of view • Linked to agency operating decisions • NCHRP Performance Based Planning Manual • Accessibility • Mobility • Economic Development

  6. Improve Reliability • Reduce variability of system performance • Delay • Travel time • Attract more riders • Reduce operations costs • Increase productivity • Link to service standards

  7. Data • Portland Tri-County Metropolitan Transit District (TriMet) • 62 million annual bus trips • 600 square miles • 1.2 million population • 700 vehicles • 98 routes • 9,000 bus stops

  8. TriMet Bus Dispatch System • Bus Dispatch System (BDS) tracks bus location and schedule adherence. • Automatic vehicle location (AVL) using global positioning system (GPS). • Automatic passenger counters (APCs) on most vehicles. Smart Bus Concept

  9. TriMet Bus Dispatch System • Real time operating information • Stop level data archived on vehicle, available for later analysis on system-wide basis • Each stop geo-coded • New data added for each stop • Scheduled arrival time (important meta data) • Actual Arrive/door open time • Number of boardings and alightings • Depart/door close time • Lift use • Schedule adherence reported to operator/dispatcher

  10. Transit Performance Measures (TPMs) System Route Segment Point

  11. System Level TPMs • System level TPMs can include all data procesed for external reporting: • Ridership • Boardings • Revenue • Expenditures of the overall system. • Route level measures can be aggregated over the entire transit network.

  12. Route Level TPMs • Time distribution between trip time and layover time • Route 12 during one weekday of service (January 24, 2002). • At the route level, using the archived BDS data, it is possible to create a daily report for each route. • Need to control layover time (non-revenue) • One day 9% of time at layovers

  13. Route Level Performance Measures

  14. Route Level TPMs • Daily report for Route 14 • Actual/scheduled hours of service • Actual/scheduled trips • Actual/scheduled miles • Actual/scheduled layover • Passengers carried • Boardings/alightings • Dwell time analysis • Delay • Average passenger load • Passengers per mile • Scheduled/actual speed • Number of operators • Inbound/outbound • Peak/offpeak • Study longitudinally over many days/years

  15. Route Level TPMs: Transit Availability • Transit Availability—key measure of quality of service • One sample census tract • 1.5 square miles • 7,900 population (2000) • 0.25-mile buffer around each bus stop • 38% of area within walking distance

  16. Route Level TPMs

  17. Route Level TPMs: Speed • Transit Operating Speed • Important for passenger attractiveness and operating efficiency • Observe how speed varies with time and space • Example using instantaneous speed/location for express bus on freeway corridor (highlights bottleneck)

  18. Route Level TPMs: Speed • Speed and travel time • Inbound vehicle trajectories. • See speed as slope. • Observe variations over a.m. peak. • Compare with off peak, day to day and beyond.

  19. Route Level TPMs: Speed • Speed and travel time • Inbound and outbound averages for Route 14 by service period. • 17.3 mph inbound. • 15.9 mph outbound. • Compare over time/system.

  20. Route Level TPMs: Schedule Adherence • Schedule Adherence • Customer perception • Operator performance • Schedule modifications • One day on one route: • 22% on time • 51% late • 27% early

  21. Route Level TPMs: Dwell Time • Dwell time • Passenger movement vs. dwell time • One route, one day. • Connect high passenger movements with delays. • Consider boarding improvements and fare payment systems Downtown

  22. Segment Level TPMs • Key Segments of Important Routes • Apply route level TPMs • Study high passenger movement areas on Route 12 • Connect land use/density • Compare stop activity with population • High passenger movement occurs at transfer points with high proportion of commercial uses

  23. Segment Level Performance Measures

  24. Point Level TPMs: Headway On-time performance • Cumulative scheduled and actual for one stop. • See arrival rate as slope. • Observe delay between two functions. • Passenger movements also shown. • Control bunching.

  25. Conclusion • Shift from relying on few, general, aggregate measures to detailed, specific measures. • Challenges in data collection deployment and archiving—demonstration of value. • Difficulties in converting large quantities of data into meaningful, useful information. • Connections to service standards. • Importance of performance measurement for planning, system design/modification and operations. • Support development of TCQSM. • Experiment with new TPMs and track them over time. • Introduce into daily operations environment.

  26. Acknowledgements Steve Callas, TriMet Thomas Kimpel, Center for Urban Studies James Strathman, Center for Urban Studies Great Cities Universities Coalition

  27. Thank You!

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