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Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction

Author

Listed:
  • Li, Xia
  • Xiao, Yuewen
  • Zhao, Xiaodong
  • Ma, Xinwei
  • Wang, Xintong

Abstract

Connected and autonomous vehicles (CAVs) are developing rapidly nowadays. In the near future, we may see human-driving vehicles (HVs) and CAVs running on the same road. The characteristics of human drivers and the interaction between HVs and CAVs, therefore, need to be further studied. All these factors will have a great impact on traffic operation. This paper aims to explore a modeling method to analyze the operation mechanism of mixed traffic flows of HVs and CAVs. Considering HV drivers’ cognitive behavioral characteristics and HV–CAV interaction effects, the forward and lateral movement rules of HVs and CAVs are proposed based on the Cellular Automata model. Then the fundamental diagram, congestion degree, lane-changing frequency, and time-spatial diagram are obtained by numerical simulation. The results show that the presence of CAVs is positively related to macroscopic traffic parameters, including velocity, flow and critical density. CAVs help to relieve traffic congestion and instabilities. The congestion degree of pure CAV traffic flow is about 1/3 that of pure HV traffic flow. When the CAV penetration rate is relatively low, the interaction between HV and CAV has a significant impact on traffic operation. Specifically, when the CAV penetration rate is below 0.5∼0.6, increasing the CAV penetration rate will increase the lane-changing frequency of the whole traffic flow.

Suggested Citation

  • Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122009268
    DOI: 10.1016/j.physa.2022.128368
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