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Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator

Author

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  • Makoto Fujiu

    (Faculty of Transdisciplinary Sciences for Innovation, Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa 920-1192, Japan)

  • Yuma Morisaki

    (Faculty of Transdisciplinary Sciences for Innovation, Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa 920-1192, Japan)

  • Jyunich Takayama

    (Graduate School of Sustainable Systems Science, Komatsu University, Komatsu 923-8511, Japan)

Abstract

Autonomous vehicles have the potential to significantly improve modes of transportation, and many businesses and research facilities are developing such systems. Although there are studies on the social implementation of autonomous vehicles, these studies are based on limited conditions such as predetermined driving environments. Therefore, in this study, we target urban areas and rural areas, and we simulate a behavioral algorithm for autonomous vehicles being developed and owned by Kanazawa University. In this study, a traffic flow simulation system (Aimsun) was constructed to reproduce the current situation of traffic flow in the city during normal times, using data from a person-trip survey conducted by the local government. In addition, we varied the mixing rate of automated vehicles and evaluated its effect on the delay time between ODs. We assume the gradual replacement of existing vehicles by autonomous vehicles on actual road networks and for realistic traffic volumes, and we investigate their impact on traffic flow. We vary the mixing rate of autonomous vehicles into actual traffic environments, and we measure the delay in the origin-destination (OD) interval to evaluate the impact of autonomous vehicles on traffic flow. The results obtained show that as the mixing rate of autonomous vehicles increases, the delay between OD intervals increases. Then, once the mixing rate exceeds a certain value, the delay between OD intervals gradually decreased. The delay time for all vehicles slightly increases as the mixing rate of autonomous vehicles increased from 10 to 45%. When the mixing rate increased from 45 to 50%, the delay time for all vehicles decreased notably, and when the mixing rate was 50 to 100%, it remained constant. Analytical results showed that when socially implementing autonomous vehicles, their mixing rate impacts the traffic flow; thus, there is a need to determine appropriate distribution scenarios and areas for implementation.

Suggested Citation

  • Makoto Fujiu & Yuma Morisaki & Jyunich Takayama, 2024. "Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator," Sustainability, MDPI, vol. 16(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:658-:d:1317563
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    References listed on IDEAS

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    1. Ye, Lanhang & Yamamoto, Toshiyuki, 2019. "Evaluating the impact of connected and autonomous vehicles on traffic safety," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    2. Kalra, Nidhi & Paddock, Susan M., 2016. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 182-193.
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    Cited by:

    1. Jong-Bae Kim, 2025. "Sustainable Real-Time Driver Gaze Monitoring for Enhancing Autonomous Vehicle Safety," Sustainability, MDPI, vol. 17(9), pages 1-29, May.

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