Study on the Relationship between the Spatial Distribution of Shared Bicycle Travel Demand and Urban Built Environment

Lili Yang, Simeng Fei, Hongfei Jia (), Jingdong Qi, Luyao Wang and Xinning Hu
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Lili Yang: College of Transportation, Jilin University, Changchun 130022, China
Simeng Fei: College of Transportation, Jilin University, Changchun 130022, China
Hongfei Jia: College of Transportation, Jilin University, Changchun 130022, China
Jingdong Qi: Changchun Municipal Engineering Design & Research Institute, Changchun 130022, China
Luyao Wang: Shenyang Urban Planning & Design Institute, Shenyang 110004, China
Xinning Hu: College of Transportation, Jilin University, Changchun 130022, China

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: As a green and sustainable trip mode, shared bicycles play an essential role in completing short-distance trips in cities. This paper proposes a method to analyze the impact of the urban built environment on the distribution of shared bicycles in a small-scale space. First, the Fishnet function of ArcGIS is utilized to divide the study area into grids of 500 m × 500 m. Then, three indicators are proposed to describe the characteristics of the urban built environment, including point of information (POI) comprehensive index, the intensity of public transportation coverage, spatial accessibility, providing them the ways to be assigned to the grids. Finally, the multivariable linear regression model and support vector regression (SVR) models are applied to reveal the impacts of built environment factors on the spatial distribution of shared bicycles. Results show that SVR models based on linear kernel function, Gaussian radial basis kernel function, and Polynomial kernel function can achieve better analysis results. The SVR model based on the Gaussian radial basis function shows higher explanatory power (adjusted R 2 = 0.978) than the multivariable linear regression model (adjusted R 2 = 0.847). This paper can aid in understanding the demand and supply of shared bicycles and help operators or governments to improve service quality.

Keywords: shared bicycle; support vector regression; built environment; space syntax (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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