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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (2): 380-391    DOI: 10.3785/j.issn.1008-973X.2023.02.017
土木与交通工程     
共享单车停车需求的多尺度时空影响因素
徐标(),路庆昌*()
长安大学 电子与控制工程学院，陕西 西安 710064
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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摘要：

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    
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
收稿日期: 2022-05-24 出版日期: 2023-02-28
CLC:  U 491.1  
基金资助: 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)
通讯作者: 路庆昌     E-mail: 2020132086@chd.edu.cn;qclu@chd.edu.cn
作者简介: 徐标（1997—），男，硕士生，从事交通大数据挖掘与行为建模研究. orcid.org/0000-0001-6161-9382. E-mail: 2020132086@chd.edu.cn
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引用本文:

徐标,路庆昌. 共享单车停车需求的多尺度时空影响因素[J]. 浙江大学学报(工学版), 2023, 57(2): 380-391.

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.

链接本文:

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

图 1  上海市研究区域
变量类别 变量名称 VIF
1）注：变量土地利用混合度为 $ - \sum\nolimits_{k = 1}^K {{p_k}\ln \;({p_k})} $，其中 $ {p_k} $为第 $ k $类POI的占比， $ K $为POI的种类数.
人口密度 居住人口密度/(人·km?2) 5.55
就业人口密度/(人·km?2) 5.99
土地利用与服务设施 土地利用混合度1） 1.90
教育类POI密度/(个·km?2) 2.84
娱乐类POI密度/(个·km?2) 13.69
医疗类POI密度/(个·km?2) 5.90
餐饮类POI密度/(个·km?2) 10.72
政府类POI密度/(个·km?2) 12.89
居住类POI密度/(个·km?2) 21.85
购物类POI密度/(个·km?2) 5.74
企业类POI密度/(个·km?2) 11.94
公园类POI密度/(个·km?2) 1.89
路口密度/(个·km?2) 13.36
主干路密度/(km·km?2) 3.55
道路属性 次干路密度/(km·km?2) 2.99
支路密度/(km·km?2) 1.84
自行车道密度/(km·km?2) 2.00
公共交通可达性 公交站点密度/(个·km?2) 6.96
地铁站点密度/(个·km?2) 3.28
停车场密度/(个·km?2) 14.42
区位条件 到市中心的距离/km 5.30
社会经济属性 人均GDP/(万元·人?1) 2.45
二手房均价/(元·m?2) 3.21
表 1  影响因素描述统计
图 2  共享单车停车记录
图 3  不同区域停车过程的距离偏差
图 4  停车需求的时空分布特征
图 5  时间-空间距离示意图
模型 R2 RSS/103 AICc/103
ED-GTWR 0.872 2.153 6.058
RD-GTWR 0.879 2.048 5.828
ED-MGTWR 0.889 1.830 5.235
RD-MGTWR 0.917 1.245 4.136
表 2  GTWR模型和MGTWR模型指标
解释变量 四分位数差
(RD-MGTWR) 		2SE
(OLS) 		时空非
平稳性
到市中心的距离 0.236 0.040 是
就业人口密度 0.189 0.042 是
居住人口密度 0.208 0.026 是
人均GDP 0.021 0.016 是
房屋均价 0.038 0.022 是
教育类POI密度 0.063 0.040 是
购物类POI密度 0.204 0.050 是
公园类POI密度 0.066 0.044 是
医疗类POI密度 0.042 0.038 是
土地利用混合度 0.226 0.032 是
公交站点密度 0.157 0.054 是
地铁站点密度 0.178 0.038 是
主干路密度 0.216 0.038 是
次干路密度 0.345 0.038 是
支路密度 0.137 0.024 是
自行车道密度 0.121 0.032 是
表 3  解释变量的时空非平稳性检验
解释变量 RD-GTWR RD-MGTWR
bs/km bt/h βmin βavg βmin bs/km bt/h βmin βavg βmin
常数项 42.963 8.516 1.848 2.936 3.742 37.028 6.636 0.412 3.978 6.634
到市中心的距离 42.963 8.516 ?1.730 ?0.864 ?0.676 14.759 3.758 ?2.589 ?0.618 ?0.364
就业人口密度 42.963 8.516 0.123 0.219 0.334 68.363 4.316 0.013 0.241 0.676
居住人口密度 42.963 8.516 0.136 0.257 0.390 22.593 13.632 0.006 0.345 0.928
人均GDP 42.963 8.516 0.087 0.152 0.371 70.131 21.254 0.116 0.136 0.155
房屋均价 42.963 8.516 0.112 0.187 0.223 71.302 22.128 0.047 0.061 0.098
教育类POI密度 42.963 8.516 0.082 0.115 0.204 39.259 8.957 0.075 0.149 0.182
购物类POI密度 42.963 8.516 ?0.003 0.127 0.211 48.889 2.124 ?0.226 0.011 0.271
医疗类POI密度 42.963 8.516 0.056 0.129 0.213 47.768 8.847 0.178 0.209 0.257
公园类POI密度 42.963 8.516 ?0.079 ?0.014 ?0.002 43.621 7.324 ?0.118 ?0.035 ?0.013
土地利用混合度 42.963 8.516 ?0.191 ?0.163 ?0.124 21.132 8.314 0.002 0.316 0.728
公交站点密度 42.963 8.516 ?0.012 ?0.009 0.127 30.781 7.293 ?0.183 0.016 0.229
地铁站点密度 42.963 8.516 0.002 0.087 0.113 41.068 2.712 ?0.208 ?0.004 0.189
主干路密度 42.963 8.516 ?0.236 ?0.153 ?0.007 53.265 1.439 ?0.571 ?0.316 ?0.016
次干路密度 42.963 8.516 ?0.119 ?0.104 ?0.096 16.296 7.126 ?0.988 ?0.342 ?0.022
支路密度 42.963 8.516 0.071 0.079 0.089 22.963 10.378 0.003 0.173 0.370
自行车道密度 42.963 8.516 0.165 0.183 0.216 49.375 1.928 0.009 0.116 0.305
表 4  RD-GTWR和RD-MGTWR模型结果
图 6  欧氏距离和骑行距离约束下MGTWR模型的局部拟合优度
图 7  变量估计参数的空间分布特征
图 8  变量估计参数的时间分布特征
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