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A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

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  • Han Su

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China)

  • Qian Zhang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China)

  • Wanying Wang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China)

  • Xiaoan Tang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China)

Abstract

Determining the distribution fitting of traditional private vehicle user driving behavior is an effective way to understand the differences between different users and provides valuable information on user travel demands. The classification of users is significant to product improvement, precision marketing, and driving recommendations. This study proposed a method which includes four aspects: (1) data collection; (2) data preprocessing; (3) data analysis—a two-stage hybrid user classification, and (4) distribution fitting method. A two-stage hybrid user classification method is used to cluster traditional vehicle users. First, the first-stage classification of the classification method extracts the daily typical time–mileage-series travel patterns (TMTP) to obtain user driving time characteristics. This first-stage classification also extracts the mean and standard deviation of the daily vehicle mileage traveled (DVMT) to express user driving demands. Next, users are divided by K-means based on the driving time characteristics and driving demands from the first stage. Finally, a three-parameter log-normal distribution is used to fit the DVMT of different user types. Comparison with traditional clustering based on the mean and standard deviation and the proportion of each vehicle’s time series in the TMTP types, this study reveals that the new methods provide significant advantages in analyzing driving behavior and high reference value for enterprises making electric vehicle driving range recommendations, car market segmentation, and policy making decisions.

Suggested Citation

  • Han Su & Qian Zhang & Wanying Wang & Xiaoan Tang, 2021. "A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7018-:d:579736
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    References listed on IDEAS

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    1. Chia-Yu Hsu & Sirirat Sae Lim & Chin-Sheng Yang, 2017. "Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 55(23), pages 7096-7109, December.
    2. Bansal, Prateek & Kockelman, Kara M. & Schievelbein, Will & Schauer-West, Scott, 2018. "Indian vehicle ownership and travel behavior: A case study of Bengaluru, Delhi and Kolkata," Research in Transportation Economics, Elsevier, vol. 71(C), pages 2-8.
    3. Jiahang He & Toshiyuki Yamamoto & Tomio Miwa & Takayuki Morikawa, 2020. "Hazard Duration Model with Panel Data for Daily Car Travel Distance: A Toyota City Case Study," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    4. Greene, David L., 1985. "Estimating daily vehicle usage distributions and the implications for limited-range vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 347-358, August.
    5. Jensen, Anders Fjendbo & Mabit, Stefan Lindhard, 2017. "The use of electric vehicles: A case study on adding an electric car to a household," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 89-99.
    6. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    7. Yang, Jun & Liu, Antung A. & Qin, Ping & Linn, Joshua, 2020. "The effect of vehicle ownership restrictions on travel behavior: Evidence from the Beijing license plate lottery," Journal of Environmental Economics and Management, Elsevier, vol. 99(C).
    8. Plötz, Patrick & Jakobsson, Niklas & Sprei, Frances, 2017. "On the distribution of individual daily driving distances," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 213-227.
    9. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    10. Wang, Yi & Zhang, Ning & Zhuo, Zhenyu & Kang, Chongqing & Kirschen, Daniel, 2018. "Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch," Applied Energy, Elsevier, vol. 210(C), pages 1141-1150.
    11. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    12. Lin, Boqiang & Wu, Wei, 2021. "The impact of electric vehicle penetration: A recursive dynamic CGE analysis of China," Energy Economics, Elsevier, vol. 94(C).
    13. Khan, Mubassira & Machemehl, Randy, 2017. "Commercial vehicles time of day choice behavior in urban areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 68-83.
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