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Cooperative vehicles for robust traffic congestion reduction: an analysis based on algorithmic, environmental and agent behavioral factors

Desai, Prajakta, Loke, Seng W. and Desai, Aniruddha 2017, Cooperative vehicles for robust traffic congestion reduction: an analysis based on algorithmic, environmental and agent behavioral factors, PLoS one, vol. 12, no. 8, pp. 1-19, doi: 10.1371/journal.pone.0182621.

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Title Cooperative vehicles for robust traffic congestion reduction: an analysis based on algorithmic, environmental and agent behavioral factors
Author(s) Desai, Prajakta
Loke, Seng W.ORCID iD for Loke, Seng W. orcid.org/0000-0001-9568-5230
Desai, Aniruddha
Journal name PLoS one
Volume number 12
Issue number 8
Article ID e0182621
Start page 1
End page 19
Total pages 19
Publisher PLoS
Place of publication San Francisco, Calif.
Publication date 2017
ISSN 1932-6203
Keyword(s) Algorithms
Australia
Automobiles
Cities
Environment
Humans
Time Factors
Summary Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies.
Language eng
DOI 10.1371/journal.pone.0182621
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30102598

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.