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

Document Type

Journal Article

Publication Date


Subject Area

place - europe, place - urban, ridership - behaviour, technology - automatic vehicle monitoring, technology - intelligent transport systems


Passenger delays, Passenger path choice model, Public transport, Real-time information, AVL data, Agent-based simulation


Public transport services are often uncertain, causing passengers’ travel times and routes to vary from day to day. However, since door-to-door passenger delays depend on both intended and realised routes, they are difficult to calculate, as opposed to vehicle delays which can be derived directly from the widely available Automated Vehicle Location (AVL) data of the public transport system. In this study we use three months of such historical AVL data to calculate corresponding realised routes and passengers delays in a large-scale, multi-modal transport network by formulating and implementing an adaptive passenger path choice model in an agent-based scenario of Metropolitan Copenhagen with 801,719 daily trips. The proposed model allows analysing five different levels of real-time information provision, ranging from no information at all to global real-time information being available everywhere. The results of more than 258 million (positive or negative) passenger delays show that variability of passengers’ travel time is considerable and much larger than that of the public transport vehicles. It is also shown that 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. Additionally, being able to automatically obtain real-time passenger information while walking and being on-board public transport services is found not to lead to considerable improvements compared to acquiring such information manually while waiting at stops, although slight benefits are demonstrated in supplementary models run with pseudo-intelligent vehicle delay forecasting.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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