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A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios

Author

Listed:
  • Yanjun Shi

    (School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Lingling Lv

    (School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Hao Yu

    (School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Liangjie Yu

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China)

  • Zihui Zhang

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China)

Abstract

Roadside Units Deployment (RSUD) is of great importance to smart transportation with the Internet of Things (IoT). It is believed to be not feasible for RSUD to cover and perceive the whole area due to the high installation and maintenance costs. The candidate locations set of RSUD may be huge for a future urban area with vehicle-to-everything (V2X) networks. Most of the previous studies tried to maximize the Roadside Units (RSU) coverage only and made few reports on emergency scenarios, such as accidents happening. We tried to find better candidate locations of RSUD in some grid road networks with equal length streets, and then chose some of these locations for final installation with a given budget to minimize the average reporting time of emergency messages in V2X networks. Firstly, we analyzed candidate locations of RSUD for different cases of RSUs and vehicles. Then we proposed a message dissemination model for RSUD with the V2X network, and a center-rule-based neighborhood search algorithm (CNSA for short). In this algorithm, we generated initial solutions with the center rule and then obtained better neighbor solutions. Numerical simulation results from small-scale urban streets showed that the proposed algorithm performs well on execution time. Simulation results with Veins and Simulation of Urban Mobility) (SUMO) verified the proposed model and CNSA for evaluating the RSUD scheme by distance instead of accident reporting time in urban areas with large-scale traffic flow.

Suggested Citation

  • Yanjun Shi & Lingling Lv & Hao Yu & Liangjie Yu & Zihui Zhang, 2020. "A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios," Mathematics, MDPI, vol. 8(10), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1734-:d:425774
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    Citations

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    Cited by:

    1. Mingwei Feng & Haiqing Yao & Ioan Ungurean, 2022. "A Roadside Unit Deployment Optimization Algorithm for Vehicles Serving as Obstacles," Mathematics, MDPI, vol. 10(18), pages 1-24, September.
    2. Dávid Baranyai & Tibor Sipos, 2022. "Black-Spot Analysis in Hungary Based on Kernel Density Estimation," Sustainability, MDPI, vol. 14(14), pages 1-13, July.
    3. Luyu Zhang & Youfu Lu & Ning Chen & Peng Wang & Weilin Kong & Qingbin Wang & Guizhi Qin & Zhenhua Mou, 2023. "Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

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