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Influence of driving style on traffic flow fuel consumption and emissions based on the field data

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  • Yang, Yichen
  • Cao, Tianyu
  • Xu, Shangzhi
  • Qian, Yeqing
  • Li, Zhipeng

Abstract

Driving style (DS) is an important factor affecting vehicle fuel consumption and emissions (FCE). Here, the relationship between DS and traffic flow FCE under different scenarios has been explored by using open-source field data. DS classification model and two car-following (CF) models for aggressive and cautious DSs were trained by using the field data. Furthermore, we performed numerical simulation experiments, where the speed profiles of the leading vehicles come from the field data. The results indicate that DS has no significant correlation with CO or HC emissions, but has strong correlations with fuel consumption, NOx, and CO2 emissions (FNC). Moreover, the influence of DS on FNC is related to the leader state which can be described by four kinematic parameters: Drive time spent accelerating, Drive time spent decelerating, Average positive acceleration, and Average negative acceleration. Specifically, the penetration rate of aggressive drivers contributes to a 3% absolute change of the FNC, and the position leads to a 1.5% absolute change of the FNC, while the sign of the change is determined by the leading vehicle state. As the intelligent transportation system attracts more and more attention, these findings can give insight for traffic managers and vehicle designers into realizing eco-driving.

Suggested Citation

  • Yang, Yichen & Cao, Tianyu & Xu, Shangzhi & Qian, Yeqing & Li, Zhipeng, 2022. "Influence of driving style on traffic flow fuel consumption and emissions based on the field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    • Handle: RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122003685
    DOI: 10.1016/j.physa.2022.127520
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    Cited by:

    1. Liu, Hongjie & Yuan, Tengfei & Zeng, Xiaoqing & Guo, KaiYi & Wang, Yizeng & Mo, Yanghui & Xu, Hongzhe, 2024. "Eco-driving strategy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    2. Guo, Wenfeng & Song, Xiaolin & Cao, Haotian & Zhao, Song & Yi, Binlin & Wang, Jianqiang, 2023. "Human-centered driving authority allocation for driver-automation shared control: A two-layer game-theoretic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. 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).

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