Journal of South China University of Technology (Natural Science Edition) ›› 2021, Vol. 49 ›› Issue (10): 1-10.doi: 10.12141/j.issn.1000-565X.200279

Special Issue: 2021年交通运输工程

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Extended Co-evolutionary Algorithm for Path Planning Based on the Urban Traffic Environment Evolution

WEN Huiying1 LIN Yifeng1 WU Haoshu2 JIANG Han1 WU Jiabin1   

  1. 1． School of Civil Engineering and Transportation，South China University of Technology，Guangzhou 510640，Guangdong，China; 2． College of Forestry and Landscape Architecture，South China Agricultural University，Guangzhou 510640，Guangdong，China
  • Received:2020-06-03 Revised:2020-10-23 Online:2021-10-25 Published:2021-09-30
  • Contact: 吴嘉彬 ( 1993-) ，男，博士生，主要从事道路交通安全、路径规划研究。 E-mail:ctwjb@mail.scut.edu.cn
  • About author:温惠英 ( 1965-) ，女，教授，博士生导师，主要从事交通运输规划与管理、交通安全研究。E-mail:hywen@scut.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China ( 51578247，71701070)

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Abstract: Ｒeasonable path planning can shorten the travelling time to ensure that rescue forces can arrive at the scene in time and improve the efficiency of emergency rescue． Based on the urban road traffic characteristics and the dynamic evolution of traffic environments，this paper proposed an extended co-evolutionary algorithm ( ECEA) to calculate the optimal rescue path． The ECEA establishes a co-evolutionary optimization mechanism，which means that the path planning process co-evolves with the evolution of traffic environments． Meanwhile，ECEA can flexibly select the search scope to improve the number and quality of alternative solutions． Experimental results show that ECEA outperforms timing co-evolutionary algorithm ( TCEPO) and Online re-optimization ( OLRO) both in the travelling time and robustness under the condition of limited data，thereby improving emergency rescue efficiency．

Key words: emergency rescue, traffic environment evolution, path planning, co-evolutionary optimization

CLC Number: