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Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model

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  • Zhu, Ning
  • Fu, Chenyi
  • Ma, Shoufeng

Abstract

Travel time is one of the most intuitive pieces of traffic information to help decision makers to control real-time traffic conditions and to guide travelers to choose a reasonable route. An optimal sensor location scheme can obtain reliable route travel time information. Most current travel-time-oriented sensor location models are deterministic and assume a given and correct travel time probability density function. Nevertheless, due to widespread observational and systematic errors, prior travel time information is not accurate or reliable. In our study, a novel data-driven link-based network sensor location method is proposed to maximize travel time information gain. The effect of route differentiation is considered, and the sensors are located at links rather than at nodes. In addition, to account for the uncertainty in the prior travel time distribution, the distributionally robust travel time information gain sensor location (DRTTIGSL) model is presented. The prior distribution information is taken into account based on a statistical measure called ϕ-divergence. The ϕ-divergence is used to construct the uncertainty set. The reformulation of DRTTIGSL is dependent on the choice of ϕ-divergence and is tractable. Extensive numerical experiments are conducted to verify the effectiveness of the DRTTIGSL model. Compared with the optimal solutions for the deterministic model, the optimal solutions for the DRTTIGSL model can reduce the worst-case situation with a small price of the average objective value, especially when the total budget is not large.

Suggested Citation

  • Zhu, Ning & Fu, Chenyi & Ma, Shoufeng, 2018. "Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 91-120.
    • Handle: RePEc:eee:transb:v:113:y:2018:i:c:p:91-120
    DOI: 10.1016/j.trb.2018.05.009
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    Citations

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    Cited by:

    1. Li, Zheng & Hensher, David A. & Rose, John M., 2010. "Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(3), pages 384-403, May.
    2. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    3. Nabavi, S.M. & Vahdani, Behnam & Nadjafi, B. Afshar & Adibi, M.A., 2022. "Synchronizing victim evacuation and debris removal: A data-driven robust prediction approach," European Journal of Operational Research, Elsevier, vol. 300(2), pages 689-712.
    4. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
    5. Zhu, Ning & Fu, Chenyi & Zhang, Xuanyi & Ma, Shoufeng, 2022. "A network sensor location problem for link flow observability and estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 428-448.
    6. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    7. Fu, Hao & Lam, William H.K. & Shao, Hu & Kattan, Lina & Salari, Mostafa, 2022. "Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).

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