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Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior

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  • Yiwen Zhou

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Fengxiang Guo

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Simin Wu

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Wenyao He

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Xuefei Xiong

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Zheng Chen

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Dingan Ni

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430062, China)

Abstract

The widespread adoption of electric public buses has a positive effect on energy conservation and emission reduction, which is significant for sustainable development. This study aims to assess the safety and economy of electric buses based on drivers’ behavior. To this end, based on the remotely acquired travel data of buses, the driving operation behavior is analyzed, and four safety and four economic characteristic indicators are respectively extracted via safety analysis, correlation examination, and an R 2 test. Then, the extreme learning machine (ELM) is leveraged to establish the safety evaluation model, and Elman neural network is employed to construct the economic evaluation model. A comparative analysis and a comprehensive evaluation are conducted for the behaviors of ten drivers. The results highlight that the proposed evaluation model that us based on the ELM and Elman neural network algorithm can efficiently distinguish the safety and economy of driving behavior. Furthermore, a graph of driving operation behavior is constructed and the analysis results also manifest that the change of driving operation behavior of buses with higher safety and economy lead to relatively stable characteristics. When the fluctuation of vehicle speed is smooth, the driver can implement moderate driving operation in real-time. One critical conclusion that was revealed through the study is that there exists a certain correlation between driving safety and economy, and buses with higher safety tend to be more economical. This study can provide a theoretical basis for planning the maneuvering and operation of electric buses, including driving speed, braking, and acceleration operations.

Suggested Citation

  • Yiwen Zhou & Fengxiang Guo & Simin Wu & Wenyao He & Xuefei Xiong & Zheng Chen & Dingan Ni, 2022. "Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10772-:d:901016
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    References listed on IDEAS

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    1. Wei Nai & Zan Yang & Yinzhen Wei & Jierui Sang & Jialu Wang & Zhou Wang & Peiyu Mo, 2022. "A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    2. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    3. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    4. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    5. Dong, Haoxuan & Zhuang, Weichao & Chen, Boli & Wang, Yan & Lu, Yanbo & Liu, Ying & Xu, Liwei & Yin, Guodong, 2022. "A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections," Applied Energy, Elsevier, vol. 310(C).
    6. Stanisław KRAWIEC & Bogusław ŁAZARZ & Sylwester MARKUSIK & Grzegorz KAROŃ & Grzegorz SIERPIŃSKI & Krzysztof KRAWIEC & Ryszard JANECKI, 2016. "Urban Public Transport With The Use Of Electric Buses – Development Tendencies," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 11(4), pages 127-137, December.
    7. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    8. Sanguinetti, Angela & Queen, Ella & Yee, Christopher & Akanesuvan, Kantapon, 2020. "Average impact and important features of onboard eco-driving feedback: A meta-analysis," Institute of Transportation Studies, Working Paper Series qt9hm406d5, Institute of Transportation Studies, UC Davis.
    9. Bian, Yiyang & Yang, Chen & Zhao, J. Leon & Liang, Liang, 2018. "Good drivers pay less: A study of usage-based vehicle insurance models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 20-34.
    10. Kurani, Kenneth S & Sanguinetti, Angela & Park, Hannah, 2015. ""Actual Results May Vary": A Behavioral Review of Eco-Driving for Policy Makers," Institute of Transportation Studies, Working Paper Series qt39z9766p, Institute of Transportation Studies, UC Davis.
    11. Sivak, Michael & Schoettle, Brandon, 2012. "Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy," Transport Policy, Elsevier, vol. 22(C), pages 96-99.
    12. Li, Lifu & Liu, Qin, 2019. "Acceleration curve optimization for electric vehicle based on energy consumption and battery life," Energy, Elsevier, vol. 169(C), pages 1039-1053.
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    Cited by:

    1. Hongli Liu & Weiguo Yun & Bin Li & Mengling Dai & Yangyuhang Wang, 2023. "A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

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