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Optimizing location of variable message signs using GPS probe vehicle data

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  • Lingling Fan
  • Liang Tang
  • Shaokuan Chen

Abstract

A multi-objective optimization model is proposed to allocate the location of VMSs by maximizing the average traffic guidance utility of VMSs and the number of benefited links, while minimizing information redundancy. The traffic guidance utility is defined to quantitatively measure the value of an installed VMS, which is calculated from passively collected GPS data and the physical topology of road network. The number of benefited links is to measure how many links are covered by upstream VMS to disseminate information. Information redundancy is introduced to quantify the mutual impairing between any two VMSs. A heuristic search algorithm is developed to solve the optimization model, which can calculate the saturated number of VMS for a road network and optimize the project schedule of VMS installation process based on the proposed objectives. A real-world case study is conducted in Beijing to illustrate the validity of the proposed approach, where taxis are used as probe vehicles to provide GPS data. The results show the effectiveness of the proposed multi-objective optimization model and it is promising to use the emerging GPS data to help agencies to allocate the locations of VMSs on both urban roads and highway networks, instead of relying on the subjective judgment from practitioners.

Suggested Citation

  • Lingling Fan & Liang Tang & Shaokuan Chen, 2018. "Optimizing location of variable message signs using GPS probe vehicle data," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    • Handle: RePEc:plo:pone00:0199831
    DOI: 10.1371/journal.pone.0199831
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    References listed on IDEAS

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    1. Kai Liu & Meng-Ying Cui & Peng Cao & Jiang-Bo Wang, 2016. "Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-12, June.
    2. Jinjun Tang & Shen Zhang & Yajie Zou & Fang Liu, 2017. "An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-11, December.
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