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Nonlinear Effects of Community Built Environment on Car Usage Behavior: A Machine Learning Approach

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Listed:
  • Keliang Liu

    (School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Jian Chen

    (School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Rui Li

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Tao Peng

    (School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Keke Ji

    (Tianjin Transportation Research Institute, Tianjin 300074, China)

  • Yuyue Gao

    (School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

This study aims to guide the community life circle to create a green, travel-supportive built environment. It quantitatively analyzes the variations in car usage behavior based on the level of the built environment of the community and objectively reflects the car usage behavior based on the parking space utilization rate (PSUR). Ordinary least squares (OLS) and gradient boosting decision tree (GBDT) models were developed to describe the impact of the built environment on this utilization rate. An empirical analysis of the model was also conducted using the multisource, heterogeneous parking data of commercial parking facilities in the main urban area of Chongqing, China; the data include records of parking survey, points of interest, and road networks. The results showed that the GBDT model had a better fitting degree than the OLS model considering nonlinear effects. In terms of the contribution of community-built environment variables, distance to business center (14.30%), population density (14.20%), and land use mix (12.60%) considerably affect the PSUR, indicating that these variables have an important influence on the use of private cars. All built environment variables have nonlinear relationships, and the threshold effects reflect a complex relationship between the built environment and car usage behavior. This study provides refined suggestions for the spatial design and transformation of the community life circle.

Suggested Citation

  • Keliang Liu & Jian Chen & Rui Li & Tao Peng & Keke Ji & Yuyue Gao, 2022. "Nonlinear Effects of Community Built Environment on Car Usage Behavior: A Machine Learning Approach," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6722-:d:828652
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    References listed on IDEAS

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