Study of Bus Passenger Origin-Destination and Travel Behavior Using Automated Data Collection Systems in London

This research explores the application of archived data from Automated Data Collection Systems (ADCS) to transport planning with a focus on bus passenger travel behavior, including Origin-Destination (OD) inference at the bus-route level, using London as a case study. It demonstrates the feasibility and ease of applying trip-chaining to infer bus passengers’ boarding and alighting locations from smart card fare transactions and automatic vehicle location data, and is the first known attempt to validate the results by comparing them with the manual passenger survey data. With the inferred OD matrices, the variations of weekday and weekend bus route OD patterns over a two-week period are examined for planning purposes. Given these variations, reliance on ADCS can provide transit planners with more comprehensive, reliable and accurate information for service planning than traditional manual surveys. Moreover, while interchange conditions and performance are considered important inputs for public transit planning, collecting such data has not been easy. Based on the inferred OD matrices and the Automatic Vehicle Location (AVL) data, alighting times for bus passengers can also be estimated. As a result, bus journey stages can easily be linked based on the difference between the subsequent trip’s boarding time and the previous trip’s alighting time for each bus passenger. By comparing the interchange time and the connecting bus route’s headway, this research also provides a way to evaluate connections between bus services. The results of this research can be expanded to the full bus network and to other travel modes, providing new and more comprehensive databases for use in intermodal network planning.