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Predictive Multimodal Trip Planner: A New Generation of Urban Routing Services

Abstract : Route planning in public transport receive an increasing interest in smart cities and particularly in metropolitan cities where crowded and jammed traffic is daily recorded in transportation network. The availability of digital footprints such as ticketing logs, or load on board the trains provide a relevant opportunity to develop innovative decision-making tools for urban routing of passengers in order to assist them to better planning their journeys. This planning must consider the forthcoming evolution of the traffic in order to adapt its response to the next state of the network and thus avoid unpleasant situations for the passengers. In this paper, we present a system for individual trip planning that incorporates short and long terms forecasting of different indicators related to the station attendance, occupancy of the trains, and delays in train schedule.
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Contributor : Ahmed Amrani <>
Submitted on : Friday, October 11, 2019 - 9:55:20 AM
Last modification on : Tuesday, December 22, 2020 - 2:59:10 PM


  • HAL Id : hal-02311598, version 1



Ahmed Amrani, Kevin Pasini, Mostepha Khouadjia. Predictive Multimodal Trip Planner: A New Generation of Urban Routing Services. TRANSITDATA2019 5th International Workshop and Symposium, Jul 2019, paris, France. ⟨hal-02311598⟩



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