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    Next Road Rerouting: A Multi-Agent System for Mitigating Unexpected Urban Traffic Congestion

    Wang, Shen, Djahel, Soufiene ORCID logoORCID: https://orcid.org/0000-0002-1286-7037, Zhang, Zonghua and McManis, Jennifer (2016) Next Road Rerouting: A Multi-Agent System for Mitigating Unexpected Urban Traffic Congestion. IEEE Transactions on Intelligent Transportation Systems, 17 (10). pp. 2888-2899. ISSN 1524-9050

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    Abstract

    During peak hours in urban areas, unpredictable traffic congestion caused by en-route events (e.g. vehicle crashes) increases drivers’ travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle re-routing system called Next Road Rerouting (NRR) is proposed to aid drivers in making the most appropriate next road choice so as to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function which takes into account the driver’s destination and local traffic conditions. In addition, the newly designed Multi-Agent System (MAS) architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared to the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and non-rerouted vehicles are also assessed and the corresponding results reveal its higher practicability.

    Item Type: Article
    Peer-reviewed: Yes
    Date Deposited: 11 May 2016 10:59
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Additional Information: This is an Author Final Copy of an article published in IEEE Transactions on Intelligent Transportation Systems, published by and copyright IEEE. "(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
    Divisions: Faculties > Science and Engineering
    Research Centres > Centre for Advanced Computational Science
    Subject terms: 0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services, Logistics & Transportation
    URI: https://e-space.mmu.ac.uk/id/eprint/609043
    DOI: https://doi.org/10.1109/TITS.2016.2531425
    ISSN 1524-9050
    e-ISSN 1558-0016

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