Shortest hyperpaths in a multimodal hypergraph with real-time information on some transit lines

David López and Angélica Lozano

Transportation Research Part A: Policy and Practice, 2020, vol. 137, issue C, 541-559

Abstract: This paper presents an algorithm to find multimodal shortest hyperpaths in a transport system where transit arrival is random (i.e. random-arrival transit system), while real-time information on vehicle arrivals is only available for some lines and modes. When the algorithm is queried, there is a short horizon where real-time information is accurate, and past this the most reliable information for estimating the arrivals is to use a random distribution specific to the lines. This problem occurs frequently in emerging cities where the transit schedules are not maintained. A Combined Real-Time Hypergraph is constructed to model the multimodal transport system where all the public transport modes have random arrivals and real-time information on arrivals is available for some lines of some modes. In order to model the period of time when some lines have real-time information, the composition of the head nodes of hyperarcs changes over time. The algorithm is tested on a real-life transport system where we change the number of lines with available real-time information to assess the performance of the algorithm in different scenarios. We found that incrementing the number of lines with real-time information does not impact the performance.

Keywords: Hypergraph; Real-time; Multimodal; Transit; Algorithm; Shortest-paths (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.tra.2019.09.020

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