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A Mixed Traffic Flow Capacity Vehicle Flow Control Strategy Combining Vehicle Networking Technology and Autonomous Driving Technology

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

The development of new energy technology has improved the market share of autonomous vehicle, but the safety and efficiency of the mixed traffic flow composed of autonomous vehicle and manned vehicles still have large hidden dangers and shortcomings. Based on this, this study proposes a control strategy for mixed traffic flow under autonomous vehicle. By constructing a variational inequality model for multi-user mixed traffic flow allocation, designing mixed traffic control ratios, and introducing Markov chains, dedicated lane settings can be achieved. Considering the changes in traffic flow under various driving behaviors, the research on dedicated lane setting strategies are conducted. The simulation results showed that the convergence times of this solving algorithm were significantly less than other comparative algorithms, and the accuracy of solving road segment nodes was better. In addition, under dual lane conditions, the proposed flexible control strategy tended to have consistent flow curves in both conservative and aggressive modes when the permeability was greater than 0.8, with a maximum flow difference of 700 veh/h. Under four lanes, the flexible control strategy could effectively meet the changes in permeability. At a density of 60 veh/km, it could reach a maximum flow rate of 4700 veh/h, and the overall flow rate change was relatively uniform and smooth. Research on optimizing mixed traffic flow through control strategies can significantly reduce the risk of traffic accidents, ensure the safety of road users, and improve the efficiency of the entire transportation system. The constructed traffic flow allocation model can orderly describe the driving behavior and road network status of different types of vehicles, which is of great value for predicting the dynamics of mixed traffic flow and alleviating traffic congestion. The research results have been verified through simulation and can provide practical application value and reference for improving the safety and efficiency of mixed traffic flow in the future.

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Data Availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

IoV:

Internet of Vehicles

MDV:

Manual Driving Vehicle

MFT:

Mixed Flow Traffic

CAV:

Connected and Automated Vehicle

AV:

Automated Vehicle

SO:

System Optimum

UE:

User Equilibrium

SO-CAV:

System Optimum-Connected and Automated Vehicle

UE-CAV:

User equilibrium-Connected and Automated Vehicle

PSO:

Particle Swarm Optimization

SUMO:

Simulation of Urban Mobility

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Acknowledgements

School of Traffic Management and Engineering, Guangxi Police College.

Funding

The research is supported by Application of intelligent traffic management technology in key construction discipline (characteristic research field) of Guangxi Police College, No.252 (2023).

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Authors and Affiliations

  1. School of Traffic Management and Engineering, Guangxi Police College, Nanning, 530023, China

    Jianyi Wu

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  1. Jianyi Wu
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Contributions

J.W. analyzed the data conducted constructive discussions, and dade significant contributions to the writing of the manuscript.

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Correspondence to Jianyi Wu.

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Wu, J. A Mixed Traffic Flow Capacity Vehicle Flow Control Strategy Combining Vehicle Networking Technology and Autonomous Driving Technology. Int. J. ITS Res. 22, 475–489 (2024). https://doi.org/10.1007/s13177-024-00412-5

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