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Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

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  • Sun, Bin
  • Zhang, Qijun
  • Wu, Zhong
  • Mao, Hongjun

Abstract

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

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  • Sun, Bin & Zhang, Qijun & Wu, Zhong & Mao, Hongjun, 2024. "Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s036054422402927x
    DOI: 10.1016/j.energy.2024.133152
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    1. Shang, Wen-Long & Song, Xuewang & Xiang, Qiannian & Chen, Haibo & Elhajj, Mireille & Bi, Huibo & Wang, Kun & Ochieng, Washington, 2025. "The impact of deep reinforcement learning-based traffic signal control on Emission reduction in urban Road networks empowered by cooperative vehicle-infrastructure systems," Applied Energy, Elsevier, vol. 390(C).

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