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An adaptive routing algorithm for in-vehicle route guidance systems with real-time information

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  • Fu, Liping

Abstract

This paper examines the problem of routing a given vehicle through a traffic network in which travel time on each link can be modeled as a random variable and its realization can be estimated in advance and made available to the vehicle's routing system before it enters the link. The underlying problem is formulated as the closed-loop adaptive shortest path routing problem (CASPRP) with the objective of identifying only the immediate link, instead of a whole path, to account for the future availability of travel time information on individual links. Having formulated the problem as a dynamic program and identified the associated difficulties, we apply an approximate probabilistic treatment to the recurrent relations and propose a labeling algorithm to solve the resultant equations. The proposed algorithm is proved theoretically to have the same computational complexity as the traditional label-correcting (LC) algorithm for the classic shortest path problems. Computational experiments on a set of randomly generated networks and a realistic road network demonstrate the efficiency of the proposed algorithm and the advantage of adaptive routing systems.

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  • Fu, Liping, 2001. "An adaptive routing algorithm for in-vehicle route guidance systems with real-time information," Transportation Research Part B: Methodological, Elsevier, vol. 35(8), pages 749-765, September.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:8:p:749-765
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    References listed on IDEAS

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    1. Stuart E. Dreyfus, 1969. "An Appraisal of Some Shortest-Path Algorithms," Operations Research, INFORMS, vol. 17(3), pages 395-412, June.
    2. Randolph W. Hall, 1986. "The Fastest Path through a Network with Random Time-Dependent Travel Times," Transportation Science, INFORMS, vol. 20(3), pages 182-188, August.
    3. Ishwar Murthy & Sumit Sarkar, 1996. "A Relaxation-Based Pruning Technique for a Class of Stochastic Shortest Path Problems," Transportation Science, INFORMS, vol. 30(3), pages 220-236, August.
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    Cited by:

    1. Bi Chen & William Lam & Agachai Sumalee & Qingquan Li & Hu Shao & Zhixiang Fang, 2013. "Finding Reliable Shortest Paths in Road Networks Under Uncertainty," Networks and Spatial Economics, Springer, vol. 13(2), pages 123-148, June.
    2. Du, Lili & Han, Lanshan & Li, Xiang-Yang, 2014. "Distributed coordinated in-vehicle online routing using mixed-strategy congestion game," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 1-17.
    3. Wu, Xing & (Marco) Nie, Yu, 2011. "Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 896-915, November.
    4. Gao, Song & Chabini, Ismail, 2006. "Optimal routing policy problems in stochastic time-dependent networks," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 93-122, February.
    5. Yan Zheng & Yanran Li & Chung-Ming Own & Zhaopeng Meng & Mengya Gao, 2018. "Real-time predication and navigation on traffic congestion model with equilibrium Markov chain," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    6. Chai, Huajun, 2019. "Dynamic Traffic Routing and Adaptive Signal Control in a Connected Vehicles Environment," Institute of Transportation Studies, Working Paper Series qt9ng3z8vn, Institute of Transportation Studies, UC Davis.
    7. Nie, Yu (Marco) & Wu, Xing, 2009. "Shortest path problem considering on-time arrival probability," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 597-613, July.
    8. Yang, Lixing & Zhou, Xuesong, 2017. "Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 68-91.
    9. Nielsen, Lars Relund & Andersen, Kim Allan & Pretolani, Daniele, 2014. "Ranking paths in stochastic time-dependent networks," European Journal of Operational Research, Elsevier, vol. 236(3), pages 903-914.
    10. Axel Parmentier, 2019. "Algorithms for non-linear and stochastic resource constrained shortest path," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(2), pages 281-317, April.
    11. Opasanon, Sathaporn & Miller-Hooks, Elise, 2006. "Multicriteria adaptive paths in stochastic, time-varying networks," European Journal of Operational Research, Elsevier, vol. 173(1), pages 72-91, August.
    12. Zhong, Shiquan & Zhou, Lizhen & Ma, Shoufeng & Jia, Ning, 2012. "Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(9), pages 1490-1505.
    13. Xinran Li & Haoxuan Kan & Xuedong Hua & Wei Wang, 2020. "Simulation-Based Electric Vehicle Sustainable Routing with Time-Dependent Stochastic Information," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    14. Manseur, Farida & Farhi, Nadir & Nguyen Van Phu, Cyril & Haj-Salem, Habib & Lebacque, Jean-Patrick, 2020. "Robust routing, its price, and the tradeoff between routing robustness and travel time reliability in road networks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 159-171.
    15. Yang, Lixing & Zhou, Xuesong, 2014. "Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 22-44.
    16. Azadian, Farshid & Murat, Alper E. & Chinnam, Ratna Babu, 2012. "Dynamic routing of time-sensitive air cargo using real-time information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 355-372.
    17. Liu, Siyuan & Qu, Qiang, 2016. "Dynamic collective routing using crowdsourcing data," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 450-469.

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