Exploring Sustainable Lane-Change Behaviours – Integrated Approach to Modelling Mixed Traffic with Intelligent-Connected Vehicles

sustainable lane-changing decision-making model mixed-traffic environment sustainable transportation intelligent-connected vehicles lane-changing impact

Authors

  • Xushan FANG College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
  • Peikun LIAN
    peikun_lian@163.com
    College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
  • Wei HUANG College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
  • Chao WANG College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
  • Said M. EASA Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada
  • Shuang LIN China YouKe Communication Technology Co., Ltd., China
  • Ning CHEN Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Downloads

This paper proposes two sustainable lane-changing (LC) decision-making models for application in intelligent-connected (I-C) and mixed-traffic environments. The models, with their unique approach, calculate the lane-changing impact area of vehicles, assess interactions with other lane-changing vehicles, and consider factors such as lane-changing conflicts, the number of connected collaborative vehicles and the overall benefits of lane-changing to determine the final lane-changing vehicle. The feasibility of lane changing is also considered when selecting the execution decision. The simulation results show that the proposed model can effectively improve the traffic efficiency. Specifically, in a mixed traffic environment, the model still maintains good lane change performance when the proportion of intelligently connected vehicles exceeds 0.85. The model effectively optimises lane changing conflicts, improves lane changing efficiency and sustainability, and contributes to the development of sustainable transportation in the future. Through micro traffic simulation, when the input flow is Q3 and the lane change ratio r is 0.2, the vehicle travel time is reduced by 24.78%, the average delay is reduced by 23.21% and the average speed is increased by 7.77%.