Multi-scale spatiotemporal influencing factors of bike-sharing parking demand

doi:10.3785/j.issn.1008-973X.2023.02.017

Journal of ZheJiang University (Engineering Science)

2023, Vol. 57

Issue (2): 380-391 DOI: 10.3785/j.issn.1008-973X.2023.02.017

Multi-scale spatiotemporal influencing factors of bike-sharing parking demand

Biao XU(

),Qing-chang LU*(

)

School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

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Abstract

In order to reveal the spatiotemporal relationship between urban multi-dimensional features and bike-sharing parking demand and their associated scales, combined with multi-source data in Shanghai, a multiscale geographically and temporally weighted regression model constrained by riding distance (RD-MGTWR) was constructed to explore the spatiotemporal heterogeneity patterns of the impact of built environment and regional economic attributes on parking demand. The model comparison analysis shows that the MGTWR model exhibits better explanatory power and reliability than the geographically and temporally weighted regression model (GTWR), and the introduction of riding distance further improves the robustness of the MGTWR model. Results show that the scale of the positive impact of socioeconomic attributes on parking demand is global, while the negative impact of location conditions presents local heterogeneity, and is most significant in the inner ring central area during the commuter morning peak. In addition, bus station density, metro station density and shopping service facility density with micro-spatial or temporal scales have positive and negative effects on parking demand. The findings of the scale effect of influencing factors can help guide parking facility zoning development and bike sharing time-sharing scheduling.

Key words： bike-sharing parking demand built environment riding distance multiscale geographically and temporally weighted regression spatiotemporal scale

Received: 24 May 2022 Published: 28 February 2023

CLC:

U 491.1

Fund: 国家自然科学基金资助项目(71971029)；陕西省自然科学研究基础研究计划资助项目(2021JC-28)

Corresponding Authors: Qing-chang LU E-mail: 2020132086@chd.edu.cn;qclu@chd.edu.cn

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Cite this article:

Biao XU,Qing-chang LU. Multi-scale spatiotemporal influencing factors of bike-sharing parking demand. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 380-391.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.02.017 OR https://www.zjujournals.com/eng/Y2023/V57/I2/380

共享单车停车需求的多尺度时空影响因素

为了揭示城市多维特征与共享单车停车需求的时空关系及其关联尺度，结合上海市多源数据，构建以骑行距离为约束的多尺度时空地理加权回归模型(RD-MGTWR)来探究建成环境和区域经济属性对停车需求影响的时空异质性模式. 模型对比分析表明，相比于时空地理加权回归模型(GTWR)，MGTWR模型表现出更好的解释力和可靠性，骑行距离的引入也进一步提高了MGTWR模型的鲁棒性. 结果显示社会经济属性对停车需求的正向影响尺度具有全局性，而区位条件的负向影响呈现局部异质性，在通勤早高峰的内环中心区域最为显著. 此外，具有微观空间或时间作用尺度的公交站点密度、地铁站点密度和购物类服务设施密度对停车需求产生了正负向影响. 影响因素尺度效应的发现有助于指导停车设施的分区规划和共享单车的分时调度.

关键词： 共享单车, 停车需求, 建成环境, 骑行距离, 多尺度时空地理加权回归, 时空作用尺度

Fig.1 Study area of Shanghai

Tab.1 Descriptive statistics of influence variables

Fig.2 Paking record of bike sharing

Fig.3 Distance deviation during parking in different regions

Fig.4 Spatial and temporal distribution characteristics of parking demand

Fig.5 Time-space distance diagram

Tab.2 Indicators of GTWR model and MGTWR model

Tab.3 Spatial temporal nonstationarity tests for explanatory variables

Tab.4 Results of RD-GTWR and RD-MGTWR models

Fig.6 Local goodness of fit of MGTWR model under Euclidean distance and riding distance constraints

Fig.7 Spatial distribution characteristics of estimated parameters of variables

Fig.8 Temporal distribution characteristics of estimated parameters of variables


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