Understanding Transit System Performance Using AVL-APC Data: An Analytics Platform with Case Studies for the Pittsburgh Region

Document Type

Journal Article

Publication Date


Subject Area

place - north america, mode - bus, mode - bus rapid transit, infrastructure - bus/tram lane, technology - automatic vehicle monitoring, technology - passenger information, operations - reliability, operations - crowding, operations - performance, operations - frequency, planning - service quality, planning - service improvement


Transit system, Automatic Vehicle Location, Automatic Passenger Counting, data analytics platform, performance metrics, bus bunching, service quality


This paper introduces a novel transit data analytics platform for public transit planning, assessing service quality and revealing service problems in high spatiotemporal resolution for public transit systems based on Automatic Passenger Counting (APC) and Automatic Vehicle Location (AVL) technologies. The platform offers a systematic way for users and decision makers to understand system performance from many aspects of service quality, including passenger waiting time, stop-skipping frequency, bus bunching level, bus travel time, on-time performance, and bus fullness. The AVL-APC data from September 2012 to March 2016 were archived in a database to support the development of a user-friendly web application that allows both users and managers to interactively query bus performance metrics for any bus routes, stops, or trips for any time period. This paper demonstrates a case study using the platform to examine bus bunching in a transit system operated by the Port Authority of Allegheny County (PAAC) in Pittsburgh. It is found that the incidence of bus bunching is heavily impacted by the location on the route as well as the time of day, and the bunching problem is more severe for bus routes operating in mixed traffic than for bus rapid transit, which operates along a dedicated busway. Furthermore, a second case study is presented with a comprehensive analysis on a representative route in Pittsburgh under schedule changes. Suggestions for operation of this route to improve service quality are proposed based on the data analytics results.


Permission to publish the abstract has been given by the Journal of Public Transportation, copyright remains with them.