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Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario

Author

Listed:
  • Haokun Song

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

  • Fuquan Zhao

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

  • Guangyu Zhu

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

  • Haoyi Zhang

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

  • Zongwei Liu

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

Abstract

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions.

Suggested Citation

  • Haokun Song & Fuquan Zhao & Guangyu Zhu & Haoyi Zhang & Zongwei Liu, 2024. "Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario," Sustainability, MDPI, vol. 16(10), pages 1-31, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4125-:d:1394666
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    References listed on IDEAS

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    1. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    2. Andrea Papu Carrone & Jeppe Rich & Christian Anker Vandet & Kun An, 2021. "Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications," Transportation, Springer, vol. 48(6), pages 2907-2938, December.
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    More about this item

    Keywords

    L3 smart vehicles; traffic efficiency; traffic energy consumption; traffic economic benefits;
    All these keywords.

    JEL classification:

    • L3 - Industrial Organization - - Nonprofit Organizations and Public Enterprise

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