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

Lili Yang · Hongfei Jia · Jingdong Qi · Luyao Wang · Xinning Hu

12 citations0/0 files storedSustainability

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Urban Transport and Accessibility Transportation Planning and Optimization Transportation and Mobility Innovations

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 R2 = 0.978) than the multivariable linear regression model (adjusted R2 = 0.847). This paper can aid in understanding the demand and supply of shared bicycles and help operators or governments to improve service quality.

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References

1998Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework 2015Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto 2014How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal 2020Built environment effects on the integration of dockless bike-sharing and the metro 2018The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets 2016Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China 2016Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system 2016How does our natural and built environment affect the use of bicycle sharing?

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2025Comparing built environment effects on bike-sharing and electric bike-sharing usage: a spatiotemporal machine learning approach 2023Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas

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