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Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints

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  • Zhaoming Zhou

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
    College of Civil Engineering, Hunan City University, Yiyang 413000, China)

  • Jianbo Yuan

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Shengmin Zhou

    (Xiangtan Technology Research Center of Urban Planning Information, Xiangtan 411100, China)

  • Qiong Long

    (College of Civil Engineering, Hunan City University, Yiyang 413000, China)

  • Jianrong Cai

    (College of Civil Engineering, Hunan City University, Yiyang 413000, China)

  • Lei Zhang

    (College of Civil Engineering, Hunan City University, Yiyang 413000, China)

Abstract

Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development of autonomous vehicles. Firstly, a bi-level programming model of mixed traffic flow assignments constrained by link capacity is established to minimize travel time. Subsequently, the algorithm based on the incremental allocation method and method of successive averages is proposed to solve the model. Through a numerical example, the road network capacity under different modes is obtained, the impact of market penetration on travel time is analyzed, and the state and characteristics of single equilibrium flow and mixed equilibrium flow are explored. Analysis results show that the road network can be maximized based on saving travel time when all vehicles are autonomous, especially when the autonomous lane is adopted. The travel time can be shortened by increasing the market penetration of autonomous vehicles and ATIS vehicles, while the former is more effective. However, the popularization of autonomous vehicles cannot be realized in the short term; the market penetration of autonomous vehicles and ATIS vehicles can be set to 0.2 and 0.6, respectively, during the introduction period.

Suggested Citation

  • Zhaoming Zhou & Jianbo Yuan & Shengmin Zhou & Qiong Long & Jianrong Cai & Lei Zhang, 2023. "Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:2923-:d:1059364
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    References listed on IDEAS

    as
    1. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    2. Yao, Zhihong & Hu, Rong & Wang, Yi & Jiang, Yangsheng & Ran, Bin & Chen, Yanru, 2019. "Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    3. Jia, Dongyao & Ngoduy, Dong, 2016. "Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 172-191.
    4. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2020. "The relationship between car following string instability and traffic oscillations in finite-sized platoons and its use in easing congestion via connected and automated vehicles with IDM based control," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 58-83.
    5. Chen, Danjue & Ahn, Soyoung & Chitturi, Madhav & Noyce, David A., 2017. "Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 196-221.
    6. Yin, Yafeng & Yang, Hai, 2003. "Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(2), pages 165-181, February.
    7. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 588-597.
    8. Guo, Ren-Yong & Yang, Hai & Huang, Hai-Jun & Tan, Zhijia, 2015. "Link-based day-to-day network traffic dynamics and equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 248-260.
    9. Bifulco, Gennaro N. & Cantarella, Giulio E. & Simonelli, Fulvio & Velonà, Pietro, 2016. "Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 73-87.
    10. Wang, Jian & Peeta, Srinivas & He, Xiaozheng, 2019. "Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 139-168.
    11. G. E. Cantarella & E. Cascetta, 1995. "Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory," Transportation Science, INFORMS, vol. 29(4), pages 305-329, November.
    12. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    13. Yang, Hai, 1998. "Multiple equilibrium behaviors and advanced traveler information systems with endogenous market penetration," Transportation Research Part B: Methodological, Elsevier, vol. 32(3), pages 205-218, April.
    14. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    15. Chen, Zhibin & He, Fang & Yin, Yafeng & Du, Yuchuan, 2017. "Optimal design of autonomous vehicle zones in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 44-61.
    16. Mohammed Al-Turki & Nedal T. Ratrout & Syed Masiur Rahman & Imran Reza, 2021. "Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives," Sustainability, MDPI, vol. 13(19), pages 1-22, October.
    17. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Modeling connected and autonomous vehicles in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 269-277.
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    2. Yanlu Yang & Yiyuan Wang & Jun Liu & Kidong Lee, 2024. "An Empirical Study on the Structural Assurance Mechanism for Trust Building in Autonomous Vehicles Based on the Trust-in-Automation Three-Factor Model," Sustainability, MDPI, vol. 16(18), pages 1-21, September.

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