Modelling the effects of real-time crowding information in urban public transport systems

Abstract

Public transport (PT) overcrowding is a notorious problem in urban transport networks. Its negative effects upon travel experience can be potentially addressed by disseminating real-time crowding information (RTCI) to passengers. However, impacts of RTCI provision in urban PT networks remain largely unknown. This study aims to contribute by developing an extended dynamic PT simulation model that enables a thorough analysis of instantaneous RTCI consequences. In the model, RTCI is generated and disseminated across the network, and then utilised in passengers’ sequential en-route choices. A case-study demonstration of the RTCI algorithm on urban PT network model of Kraków (Poland) shows that instantaneous RTCI has the potential to improve passengers’ travel experience, although it is also susceptible to inaccuracy. RTCI provision can yield total travel utility improvements of 3% in typical PM peak-hour, with reduced impacts of the worst overcrowding effects (in terms of denied-boarding and in-vehicle travel disutility in overcrowded conditions) of 30%. Highlights: Real-time crowding information (RTCI) is an increasingly feasible solution in public transport. We introduce a novel framework for modelling the network effects of instantaneous RTCI. Instantaneous RTCI can result in improved travel experience but also substantial inaccuracy risk. Reduced impacts of the worst overcrowding experience amount to up to 30%.