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Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration

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  • Shi, Xiaowei
  • Li, Xiaopeng

Abstract

Increasingly, commercial vehicles are equipped with automated vehicle (AV) features such as adaptive cruise control systems. The AV feature can automatically control the headway between the current vehicle and the preceding vehicle in an adaptive manner. The automatic control may lead to significantly different car- following motions compared with those of human-driven vehicles, which challenges the applicability of classic traffic flow theory to emerging road traffic with AVs. To investigate the impacts of commercial AVs on traffic flow, this paper proposes a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. To demonstrate the empirical experiment settings, we collected high-resolution trajectory data with multiple commercial AVs following one another in a platoon with different headway settings. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The method proposed in this paper, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies.

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  • Shi, Xiaowei & Li, Xiaopeng, 2021. "Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 279-292.
    • Handle: RePEc:eee:transb:v:150:y:2021:i:c:p:279-292
    DOI: 10.1016/j.trb.2021.06.011
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    Cited by:

    1. Shi, Xiaowei & Li, Xiaopeng, 2023. "Trajectory Planning for an Autonomous Vehicle with Conflicting Moving Objects Along a Fixed Path – An Exact Solution Method," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 228-246.
    2. Guan, Hao & Wang, Hua & Meng, Qiang & Mak, Chin Long, 2023. "Markov chain-based traffic analysis on platooning effect among mixed semi- and fully-autonomous vehicles in a freeway lane," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 176-202.
    3. Nourinejad, Mehdi & Bahrami, Sina & Yin, Yafeng, 2023. "Optimal investment in driving automation: Individual vs. cooperative sensing," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    4. Bari, Chintaman Santosh & Chandra, Satish & Dhamaniya, Ashish, 2022. "Service headway distribution analysis of FASTag lanes under mixed traffic conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. Li, Pengbo & Tian, Lijun & Xiao, Feng & Zhu, Hongwei, 2022. "Can day-to-day dynamic model be solved analytically? New insights on portraying equilibrium and accommodating autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 374-395.

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