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
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
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.
Framework Service Inputs Labor, Capital, Fuel Cost Effectiveness Cost Efficiency Service Outputs Veh-Hrs, Veh-Miles Service Consumption Pax, Pax-Miles, Revenue Service Effectiveness
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
Improve Reliability • Reduce variability of system performance • Delay • Travel time • Attract more riders • Reduce operations costs • Increase productivity • Link to service standards
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
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
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
Transit Performance Measures (TPMs) System Route Segment Point
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.
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
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
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
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)
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.
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.
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
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
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
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.
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.
Acknowledgements Steve Callas, TriMet Thomas Kimpel, Center for Urban Studies James Strathman, Center for Urban Studies Great Cities Universities Coalition