Beyond Generating Transit Performance Measures: Visualizations and Statistical Analysis with Historical Data

Document Type

Journal Article

Publication Date


Subject Area

infrastructure - vehicle, mode - mass transit, mode - subway/metro, operations - performance, operations - reliability, organisation - performance, planning - methods, planning - service quality, technology - intelligent transport systems


Visualization, Visualisation, Vehicle locating systems, TriMet (Portland, Oregon), Tri-County Metropolitan Transportation District of Oregon, Transit, Statistical models, Statistical methods, Statistical analysis, Service reliability, Service quality, RTI, Road transport informatics, Radio communication, Quality of service, Public transit, Portland (Oregon), Performance measurement, Performance indicators, Passenger service quality, Passenger counting, Metrics (Quantitative assessment), Mathematical statistics, Mass transit, Local transit, IVHS, ITS (Intelligent transportation systems), Intelligent vehicle highway systems, Intelligent transportation systems, Dispatching, Data archiving, AVL, Automatic vehicle location, Automatic location systems, Automatic data collection systems, ATT, Advanced transport telematics


In recent years, the use of performance measures for transit planning and operations has gained a great deal of attention, particularly as transit agencies are required to provide service under increasing demand and with diminishing resources. The widespread application of the technologies of intelligent transportation systems to transit encourages automating the generation of comprehensive performance measures. In Portland, Oregon, the local transit provider, Tri-County Metropolitan Transportation District of Oregon (TriMet), has been on the leading edge of the transit industry since it implemented its bus dispatch system (BDS) in 1997. The BDS comprises automatic vehicle location on all buses, a radio communications system, automatic passenger counters on most vehicles, and a central dispatch center. Most significant, TriMet developed a system to archive all its stop-level data, which are then available for conversion to performance indicators. In the past decade, TriMet has used this system extensively to generate performance indicators through monthly, quarterly, and annual reporting. TriMet generates a wide range of performance indicators, yet an opportunity remains to explore metrics beyond general transit performance measures (TPMs). On the basis of an analysis of 1 year of archived BDS data for all routes and stops, the power of using visualization tools to understand the abundance of BDS data is demonstrated. In addition, several statistical models are generated to demonstrate the power of statistical analysis in conveying valuable and new TPMs beyond what is currently generated at TriMet or in the transit industry in general. It is envisioned that systematic use of these new methods and TPMs can help TriMet and other transit agencies improve the quality and reliability of their service.