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Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

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  • Sun, Xiuqiao
  • Wang, Jian

Abstract

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Suggested Citation

  • Sun, Xiuqiao & Wang, Jian, 2018. "Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 205-216.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:205-216
    DOI: 10.1016/j.physa.2018.02.181
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    References listed on IDEAS

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    1. Yafeng Yin, 2008. "A Scenario-based Model for Fleet Allocation of Freeway Service Patrols," Networks and Spatial Economics, Springer, vol. 8(4), pages 407-417, December.
    2. Ehsan Ardjmand & Gary Weckman & Namkyu Park & Pooya Taherkhani & Manjeet Singh, 2015. "Applying genetic algorithm to a new location and routing model of hazardous materials," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 916-928, February.
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    Cited by:

    1. Tang, Jinjun & Yang, Yifan & Qi, Yong, 2018. "A hybrid algorithm for Urban transit schedule optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 745-755.
    2. Zhou, Li & Yang, Xin & Wang, Huan & Wu, Jianjun & Chen, Lei & Yin, Haodong & Qu, Yunchao, 2020. "A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    3. Timothy C. Matisziw & Mark Ritchey & Robert MacKenzie, 2022. "Change of Scene: The Geographic Dynamics of Resilience to Vehicular Accidents," Networks and Spatial Economics, Springer, vol. 22(3), pages 587-606, September.
    4. Xiuqiao Sun & Jian Wang & Weitiao Wu & Wenjia Liu, 2018. "Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol," Sustainability, MDPI, vol. 10(11), pages 1-15, November.

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