Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model

Abstract

Public transport services are often uncertain, causing passengers’ travel times and routes to vary from day to day. This study uses three months of historical Automatic Vehicle Location (AVL) data to calculate corresponding realised routes and passengers delays in a large-scale, multi-modal public transport network by formulating and implementing an adaptive passenger path choice model, and apply it to an agent-based scenario of Metropolitan Copenhagen with 801,719 daily trips. Five different levels of real-time information are analysed, ranging from no information at all to global real-time information being available everywhere. The results of more than 258 million inferred passenger delays show that variability of passengers’ travel time is considerable and much larger than that of the public transport vehicles. Furthermore, obtaining global real-time information at the beginning of the trip reduces passengers delay dramatically, although still being inferior to receiving such along the trip.