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An Efficient Solution for One-To-Many Multi-Modal Journey Planning

Authors Jonas Sauer, Dorothea Wagner, Tobias Zündorf

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Author Details

Jonas Sauer
  • Karlsruhe Institute of Technology (KIT), Germany
Dorothea Wagner
  • Karlsruhe Institute of Technology (KIT), Germany
Tobias Zündorf
  • Karlsruhe Institute of Technology (KIT), Germany

Funding

This research was funded by the DFG under grant number WA 654123-2.

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Jonas Sauer, Dorothea Wagner, and Tobias Zündorf. An Efficient Solution for One-To-Many Multi-Modal Journey Planning. In 20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020). Open Access Series in Informatics (OASIcs), Volume 85, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/OASIcs.ATMOS.2020.1

Abstract

We study the one-to-many journey planning problem in multi-modal transportation networks consisting of a public transit network and an additional, non-schedule-based mode of transport. Given a departure time and a single source vertex, we aim to compute optimal journeys to all vertices in a set of targets, optimizing both travel time and the number of transfers used. Solving this problem yields a crucial component in many other problems, such as efficient point-of-interest queries, computation of isochrones, or multi-modal traffic assignments. While many algorithms for multi-modal journey planning exist, none of them are applicable to one-to-many scenarios. Our solution is based on the combination of two state-of-the-art approaches: ULTRA, which enables efficient journey planning in multi-modal networks, but only for one-to-one queries, and (R)PHAST, which enables efficient one-to-many queries, but only in time-independent networks. Similarly to ULTRA, our new approach can be combined with any existing public transit algorithm that allows a search to all stops, which we demonstrate for CSA and RAPTOR. For small to moderately sized target sets, the resulting algorithms are nearly as fast as the pure public transit algorithms they are based on. For large target sets, we achieve a speedup of up to 7 compared to a naive one-to-many extension of a state-of-the-art multi-modal approach.

Subject Classification

ACM Subject Classification
  • Theory of computation → Shortest paths
  • Mathematics of computing → Graph algorithms
  • Applied computing → Transportation
Keywords
  • Algorithm Engineering
  • Route Planning
  • Public Transit
  • One-to-Many

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Supplementary Materials

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