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Customizable Route Planning in Road Networks

Author

Listed:
  • Daniel Delling

    (Microsoft Research, Mountain View, California 94043)

  • Andrew V. Goldberg

    (Microsoft Research, Mountain View, California 94043)

  • Thomas Pajor

    (Microsoft Research, Mountain View, California 94043)

  • Renato F. Werneck

    (Microsoft Research, Mountain View, California 94043)

Abstract

We propose the first routing engine for computing driving directions in large-scale road networks that satisfies all requirements of a real-world production system. It supports arbitrary metrics (cost functions) and turn costs, enables real-time queries, and can incorporate a new metric in less than a second, which is fast enough to support real-time traffic updates and personalized cost functions. The amount of metric-specific data is a small fraction of the graph itself, which allows us to maintain several metrics in memory simultaneously. The algorithm is the core of the routing engine currently in use by Bing Maps.

Suggested Citation

  • Daniel Delling & Andrew V. Goldberg & Thomas Pajor & Renato F. Werneck, 2017. "Customizable Route Planning in Road Networks," Transportation Science, INFORMS, vol. 51(2), pages 566-561, May.
    • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:2:p:566-561
    DOI: 10.1287/trsc.2014.0579
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    References listed on IDEAS

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    1. Eric V. Denardo & Bennett L. Fox, 1979. "Shortest-Route Methods: 1. Reaching, Pruning, and Buckets," Operations Research, INFORMS, vol. 27(1), pages 161-186, February.
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    Cited by:

    1. Andrea Detti & Giuseppe Tropea & Nicola Blefari Melazzi & Dag Kjenstad & Lukas Bach & Ivar Christiansen & Federico Lisi, 2019. "Federation and Orchestration: A Scalable Solution for EU Multimodal Travel Information Services," Sustainability, MDPI, vol. 11(7), pages 1-16, March.
    2. Moritz Baum & Julian Dibbelt & Andreas Gemsa & Dorothea Wagner & Tobias Zündorf, 2019. "Shortest Feasible Paths with Charging Stops for Battery Electric Vehicles," Transportation Science, INFORMS, vol. 53(6), pages 1627-1655, November.
    3. Chen, Bi Yu & Chen, Xiao-Wei & Chen, Hui-Ping & Lam, William H.K., 2020. "Efficient algorithm for finding k shortest paths based on re-optimization technique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    4. Cristiana L. Lara & John Wassick, 2023. "Future of Supply Chain: Challenges, Trends, and Prospects," Papers 2301.13174, arXiv.org.

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