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Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication

Author

Listed:
  • Sehyun Tak

    (Center for Connected and Automated Driving Research, The Korea Transport Institute, Sejong 30147, Korea)

  • Jeongyun Kim

    (Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • Donghoun Lee

    (Center for Connected and Automated Driving Research, The Korea Transport Institute, Sejong 30147, Korea)

Abstract

There have been enormous efforts to implement automated vehicle-based mobility (AVM) by considering smart infrastructure such as cooperative intelligent transportation system. However, there is lack of consideration on economical approach for an optimal deployment strategy of the AVM service and smart infrastructure. Furthermore, the influence of travel demand in service area has been ignored. We develop a new framework for maximizing the profit of connected and automated vehicle-based mobility (CAV-M) service using cost modeling and metaheuristic optimization algorithm. The proposed framework extracts an optimal sub-network, which is selected by a set of optimal links in the service area, and identifies an optimal construction strategy for the smart infrastructure depending on given operational design domain and travel demand. Based on service network analyses with varying demand patterns and volumes, we observe that the optimal sub-network varies with the combination of trip demand patterns and volumes. It is also found that the benefit of deploying the smart infrastructure is obtainable only when there are sufficient travel demands. Furthermore, the optimal sub-network is always superior to raw network in terms of economical profit, which suggests the proposed framework has great potential to prioritize road links in the target area for the CAV-M service.

Suggested Citation

  • Sehyun Tak & Jeongyun Kim & Donghoun Lee, 2022. "Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3688-:d:776139
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    References listed on IDEAS

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    1. Kerner, Boris S., 2016. "Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 700-747.
    2. 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).
    3. Sehyun Tak & Soomin Woo & Sungjin Park & Sunghoon Kim, 2021. "The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System," Sustainability, MDPI, vol. 13(12), pages 1-29, June.
    4. W. C. E. Lim & G. Kanagaraj & S. G. Ponnambalam, 2016. "A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 417-429, April.
    5. Sehyun Tak & Sari Kim & Hwapyeong Yu & Donghoun Lee, 2022. "Analysis of Relationship between Road Geometry and Automated Driving Safety for Automated Vehicle-Based Mobility Service," Sustainability, MDPI, vol. 14(4), pages 1-13, February.
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    Cited by:

    1. Junhee Kang & Sehyun Tak & Sungjin Park, 2023. "Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea," Sustainability, MDPI, vol. 15(16), pages 1-21, August.

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