IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v309y2024ics036054422402927x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422402927X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133152?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    2. Liu, Rui & Liu, Hui & Nie, Shida & Han, Lijin & Yang, Ningkang, 2023. "A hierarchical eco-driving strategy for hybrid electric vehicles via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 281(C).
    3. Hsu, Chia-Yu & Yang, Chin-Sheng & Yu, Liang-Chih & Lin, Chi-Fang & Yao, Hsiu-Hsen & Chen, Duan-Yu & Robert Lai, K. & Chang, Pei-Chann, 2015. "Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system," International Journal of Production Economics, Elsevier, vol. 164(C), pages 454-461.
    4. Du, Yu & Kouvelas, Anastasios & ShangGuan, Wei & Makridis, Michail A., 2022. "Dynamic capacity estimation of mixed traffic flows with application in adaptive traffic signal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Sun, Bin & Zhang, Qijun & Hu, Le & Zou, Chao & Wei, Ning & Jia, Zhenyu & Zhao, Xiaoyang & Peng, Jianfei & Mao, Hongjun & Wu, Zhong, 2023. "A prediction-evaluation method for road network energy consumption: Fusion of vehicle energy flow principle and Two-Fluid theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    6. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    7. Zhi (Aaron) Cheng & Min-Seok Pang & Paul A. Pavlou, 2020. "Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems," Information Systems Research, INFORMS, vol. 31(3), pages 653-674, September.
    8. Zhixiang Hou & Yucai Zhou & Ronghua Du, 2016. "Special issue on intelligent transportation systems, big data and intelligent technology," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(8), pages 747-750, November.
    9. Robert Herman & Siamak Ardekani, 1984. "Characterizing Traffic Conditions in Urban Areas," Transportation Science, INFORMS, vol. 18(2), pages 101-140, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Jianqi & Yang, Hang & Cheng, Rongjun & Zheng, Pengjun & Wu, Bing, 2024. "A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    2. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    3. Peng, Jiali & Shangguan, Wei & Peng, Cong & Chai, Linguo, 2024. "Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    4. Jiayu Ding & Yuewei Wang & Chaoyue Li, 2024. "A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems," Land, MDPI, vol. 13(6), pages 1-27, May.
    5. Salvini, Pericle & Kunze, Lars & Jirotka, Marina, 2024. "On self-driving cars and its (broken?) promises. A case study analysis of the German Act on Autonomous Driving," Technology in Society, Elsevier, vol. 78(C).
    6. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    7. Elina H. Hwang & Xitong Guo & Yong Tan & Yuanyuan Dang, 2022. "Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity," Information Systems Research, INFORMS, vol. 33(2), pages 515-539, June.
    8. Guo, Xue & Cheng, Aaron & Pavlou, Paul A., 2024. "Skill-biased technical change, again? Online gig platforms and local employment," LSE Research Online Documents on Economics 124538, London School of Economics and Political Science, LSE Library.
    9. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    10. Daganzo, Carlos F., 2010. "On the Stability of Freeway Traffic," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4vf597r5, Institute of Transportation Studies, UC Berkeley.
    11. He, Yongming & Kang, Jia & Pei, Yulong & Ran, Bin & Song, Yuting, 2021. "Research on influencing factors of fuel consumption on superhighway based on DEMATEL-ISM model," Energy Policy, Elsevier, vol. 158(C).
    12. Kwangho Ko & Tongwon Lee & Seunghyun Jeong, 2021. "A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    13. Dong, Haoxuan & Shi, Junzhe & Zhuang, Weichao & Li, Zhaojian & Song, Ziyou, 2025. "Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies," Applied Energy, Elsevier, vol. 377(PA).
    14. Sun, Bin & Zhang, Qijun & Hu, Le & Zou, Chao & Wei, Ning & Jia, Zhenyu & Zhao, Xiaoyang & Peng, Jianfei & Mao, Hongjun & Wu, Zhong, 2023. "A prediction-evaluation method for road network energy consumption: Fusion of vehicle energy flow principle and Two-Fluid theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    15. Jun Zhang & Shuyang Li & Yichuan Wang, 2023. "Shaping a Smart Transportation System for Sustainable Value Co-Creation," Information Systems Frontiers, Springer, vol. 25(1), pages 365-380, February.
    16. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    17. Yang, Yichen & Li, Zuxing & Li, Yabin & Cao, Tianyu & Li, Zhipeng, 2023. "Stability enhancement for traffic flow via self–stabilizing control strategy in the presence of packet loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    18. Ma, Guangyi & Li, Keping, 2024. "Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road," Energy, Elsevier, vol. 310(C).
    19. Wang, Zhuoer & Zhang, Hongjuan & Li, Bijun & Fan, Xiaoyi & Ma, Zhenqiang & Zhou, Jian, 2024. "An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles," Energy, Elsevier, vol. 309(C).
    20. Kyung Sun (Melissa) Rhee & Jinyang Zheng & Youwei Wang & Yong Tan, 2023. "Value of Information Sharing via Ride-Hailing Apps: An Empirical Analysis," Information Systems Research, INFORMS, vol. 34(3), pages 1228-1244, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:309:y:2024:i:c:s036054422402927x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.