Nonlinear Effect of Built Environment on Bike-sharing Ridership at Different Time Periods: A Case Study from Shanghai

被引：0
|
作者：
Wu J. [1 ,2 ]
Tang G. [1 ,2 ]
Li W. [1 ,2 ]
机构：
[1] Business School, University of Shanghai for Science and Technology, Shanghai
[2] Intelligent, Emergency Management School, University of Shanghai for Science and Technology, Shanghai
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2024年 / 24卷 / 01期
基金：
中国国家自然科学基金;
关键词：
bike-sharing service; built environment; Gradient Boosting Decision Tree; nonlinear effect; urban traffic;
D O I：
10.16097/j.cnki.1009-6744.2024.01.029
中图分类号：
学科分类号：
摘要：
To investigate the nonlinear effect of built environment on bike-sharing ridership at different time periods, this study utilized 2016 data from Mobike in Shanghai, along with online public data. Using Gradient Boosting Decision Trees, prediction models for bike-sharing ridership during weekdays, weekends, and morning-evening peak hours were developed. The findings revealed that, regarding the importance of built environment, proximity to the city center had a consistent and significant influence on borrowed and returned bikes across all four time periods, with a relative importance of over 17%. Following that, road density, cycle-way ratio, and population density had substantial but varying influences over the four time periods. In terms of nonlinear effects, proximity to the city center, cycle-way ratio, population density, and job POI (Point of Interest) density all exhibited complicated nonlinear relationships with bike-sharing ridership and notable threshold effects. Meanwhile, bike usage is negatively related to road density and positively related to residence POI density. All built environment variables had varying nonlinear effects on bike borrowing and returning during morning and evening peak hours, consistent with the tidal features of bike riding. The cycle-way ratio along with the distance to CBD, and job POI density, have significant synergistic effects on peak-hour bike-sharing ridership. © 2024 Science Press. All rights reserved.
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页码：290 / 298and310
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