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Optimization of lane-changing advisory of connected and autonomous vehicles at a multi-lane work zone

Author

Listed:
  • Wenjing Wu

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, P. R. China)

  • Yongbin Zhan

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, P. R. China)

  • Lili Yang

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, P. R. China)

  • Renchao Sun

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, P. R. China)

  • Anning Ni

    (��School of Naval Architecture Ocean & Civil Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, P. R. China)

Abstract

The work zone with lane closure will be an active bottleneck due to vehicles’ mandatory lane-changing conflicts. The emerging Connected Autonomous Vehicle (CAV) technology provides opportunities for vehicle motion planning to improve traffic performance. However, the literature using CAV technology mainly focuses on single-lane lane-changing control in the merging area. The algorithm dealing with multi-lane lane-changing control is absent. In this paper, a simulation system with a lane-changing optimal strategy embedded for the multi-lane work zone is presented under the heterogeneous traffic flow. First, the road upstream of the work zone is divided into several segments, and an optimal multi-lane lane-changing algorithm is designed. It is recommended that CAVs, on the closure lane and the merged lane, change lanes on each segment to balance traffic distribution and minimize traffic delay. Second, to validate the algorithm proposed, a typical three-lane freeway with one-lane closed for the work zone is researched, and the simulation platform based on cellular automata is developed. Third, the advantages of multi-lane control strategies are studied and discussed in traffic efficiency improvement and collision risk reduction by comparing previous lane-changing control algorithms.

Suggested Citation

  • Wenjing Wu & Yongbin Zhan & Lili Yang & Renchao Sun & Anning Ni, 2022. "Optimization of lane-changing advisory of connected and autonomous vehicles at a multi-lane work zone," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-16, February.
    • Handle: RePEc:wsi:ijtafx:v:25:y:2022:i:01:n:s0129183122500206
    DOI: 10.1142/S0129183122500206
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