广州市主城区共享单车骑行目的地时空特征与影响因素

doi:10.11821/dlyj020190081

地理研究 ›› 2019, Vol. 38 ›› Issue (12): 2859-2872.doi: 10.11821/dlyj020190081

广州市主城区共享单车骑行目的地时空特征与影响因素

高枫¹, 李少英¹(), 吴志峰¹, 吕帝江², 黄冠平¹, 刘小平³

1. 广州大学地理科学学院,广州 510006
2. 广东国地规划科技股份有限公司,广州 510650
3. 中山大学地理科学与规划学院,广州 510275

收稿日期:2019-01-28 修回日期:2019-03-14 出版日期:2019-12-20 发布日期:2019-12-25
通讯作者: 李少英
作者简介:高枫（1996- ）,男,广东中山人,硕士,研究方向为地理大数据与城市研究。E-mail: vincenttheone@163.com
基金资助:
广州市科技计划项目(201904010198);国家自然科学基金项目(41871290);国家自然科学基金项目(41401432);国家自然科学基金项目(41671430);广东省普通高校特色创新类项目(2015KTSCX103)

Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city

GAO Feng¹, LI Shaoying¹(), WU Zhifeng¹, LV Dijiang², HUANG Guanping¹, LIU Xiaoping³

1. School of Geographical Science, Guangzhou University, Guangzhou 510006, China
2. Guangdong Guodi Planning Technology Co., Ltd, Guangzhou 510650, China
3. School of Geography Science and Planning, Sun Yat-Sen University, Guangzhou 510006, China

Received:2019-01-28 Revised:2019-03-14 Online:2019-12-20 Published:2019-12-25
Contact: LI Shaoying

摘要/Abstract

摘要：

已有共享单车骑行影响因素研究主要关注起点,大多忽略目的地,在探讨其影响因素的时间差异及交互作用方面较少。以广州市主城区为例,引入地理探测器,精细分析目的地分布影响因素的时间差异,并进行交互探测。研究发现：① 早高峰到达量大于晚高峰,早高峰目的地多分布在CBD,信息产业园和职住平衡地区,晚高峰多分布在地铁3号线体育西至华师站沿线和高密度住宅区。② 服务设施类是影响最显著的类别,其次是交通可达、土地利用和自然环境类别,其中影响力较大的因子依次是住宅、餐饮、公司、购物设施分布、路网密度、距地铁站口距离和POI多样性。③ 各因子影响力存在明显时间差异,所有建成环境因子在早晚高峰时段影响力均大于其他时段,其中公司企业分布因子的影响力在早高峰时段迅速增强。④ 因子间均为双因子增强关系,其中服务设施分布类别中因子交互作用最显著,服务设施分布与交通可达类别的因子交互作用次之。

关键词: 共享单车骑行目的地, 时空特征, 影响因素, 地理探测器, 广州市

Abstract:

Since the emergence of dockless bike sharing in China, it has provided convenience and non-motorized travel mode for residents' short distance trips. Bike sharing plays an important role in improving the accessibility of public transportation and reducing the motorized pollution. At the same time, it also brings out urban issues, such as excessive amount of bike sharing, and mismatch between supply and demand of bike sharing. The main reason for these problems is the lack of accurate prediction and effective scheduling for bike sharing ride. Exploring the spatial and temporal characteristics of bike sharing ride and detecting the influencing factors can provide scientific decision-making basis for precise prediction and effective scheduling of bike sharing. Even though some studies have paid attention to the influencing factors of bike-sharing ride behaviors, most of them focused on the starting point but neglected the destination. Moreover, the temporal difference of influencing factors and the interaction between the factors were seldom revealed in the previous studies. Taking mobike in Guangzhou city as an example, this study aims to analyze the spatial and temporal characteristics of the ride destination of bike sharing. We detect the temporal differences of the influencing factors of bike sharing ride destination, and further explores the interaction between the determinants by using geographical detector. The results show that: (1) The usage of bike sharing in morning-peak time is greater than that in evening-peak time, and the spatial distribution of bike sharing ride destination has obvious temporal differences. The ride destinations of bike sharing at morning peak period are mainly distributed at CBD, zone of information industry and job-housing balance areas. While the ride destinations at evening peak period are mainly distributed along Metro Line 3 from Tiyuxi station to Huashi station as well as high-density residential areas. (2) The element of service facilities has the greatest impacts on the ride destinations of bike sharing, followed by the accessibility, land use and natural environment elements. To be more specifically, the influencing degree of the factors ranks as follows: residential communities distribution, catering facilities distribution, corporate distribution, shopping facilities distribution, road density, distance to metro station entrances and POI diversity. (3) The influence of each factor has remarkable temporal differences, for example, the influence of corporate distribution factor grows rapidly during the morning peak period. (4) The interaction effect of any two factors on the ride destinations of bike sharing is greater than the effect of one single factor. Among them, the interaction effect of factors which belong to service facilities elements are the greatest, followed by the interaction effects between factors of service facilities and accessibility.

Key words: bike sharing ride destination, spatial-temporal pattern, influencing factor, geographical detector, Guangzhou

高枫, 李少英, 吴志峰, 吕帝江, 黄冠平, 刘小平. 广州市主城区共享单车骑行目的地时空特征与影响因素[J]. 地理研究, 2019, 38(12): 2859-2872.

GAO Feng, LI Shaoying, WU Zhifeng, LV Dijiang, HUANG Guanping, LIU Xiaoping. Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city[J]. GEOGRAPHICAL RESEARCH, 2019, 38(12): 2859-2872.

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类别		因子	均值	标准差
自然环境影响因子		高程（m）	156.45	108.71
		坡度（°）	3.19	4.20
		距河流距离（m）	3 023.59	3 291.31
		气温（℃）	30.13	2.28
建成环境影响因子	交通可达因子	距地铁站出口距离（m）	1 329.95	1 159.92
		距普通公交站距离（m）	324.50	321.90
		距BRT公交站距离（m）	6 041.07	3 769.47
		路网密度（km/km2）	6.63	5.72
	土地利用因子	POI多样性	0.70	0.64
		建筑高度（floor）	4.78	5.16
	服务设施因子	购物设施分布密度（/km2）	4.64	6.95
		餐饮设施分布密度（/km2）	67.08	99.90
		住宅分布密度（/km2）	13.82	21.98
		公司企业分布密度（/km2）	111.94	169.50

因子类别	早高峰（7-9时）		晚高峰（17-19时）		其他时段
因子类别	q值	q排序	q值	q排序	q值	q排序
自然环境因子	0.092	4	0.083	4	0.096	4
交通可达因子	0.312	2	0.322	2	0.265	2
土地利用因子	0.255	3	0.275	3	0.234	3
服务设施因子	0.541	1	0.580	1	0.495	1

因子		早高峰（7-9时）		晚高峰（17-19时）		其他时段
因子		q值	q排序	q值	q排序	q值	q排序
自然环境因子	高程	0.056	13	0.048	13	0.054	13
	坡度	0.039	14	0.039	14	0.035	14
	距水体距离	0.148	11	0.131	11	0.141	11
	气温	0.124	12	0.114	12	0.155	10
交通可达因子	距地铁站口距离	0.366	6	0.388	6	0.321	6
	距普通公交站距离	0.152	10	0.146	10	0.133	12
	距BRT站距离	0.285	8	0.282	8	0.216	8
	路网密度	0.443	5	0.471	5	0.391	5
土地利用因子	POI多样性	0.292	7	0.316	7	0.278	7
	建筑高度	0.218	9	0.235	9	0.190	9
服务设施因子	购物服务	0.480	4	0.535	3	0.482	3
	餐饮服务	0.544	2	0.601	2	0.508	2
	住宅小区	0.616	1	0.680	1	0.571	1
	公司企业	0.523	3	0.505	4	0.422	4

周二8时（到达量最小的早高峰）				周五8时（到达量最大的早高峰）
排序	主导交互因子	q值	交互结果	排序	主导交互因子	q值	交互结果
1	公司企业分布∩住宅小区分布	0.7740	双因子增强	1	公司企业分布∩住宅小区分布	0.7282	双因子增强
2	路网密度∩公司企业分布	0.7171	双因子增强	2	餐饮设施分布∩住宅小区分布	0.7076	双因子增强
3	住宅小区分布∩距BRT站距离	0.7077	双因子增强	3	路网密度∩餐饮设施分布	0.7007	双因子增强
4	路网密度∩餐饮设施分布	0.6982	双因子增强	4	购物设施分布∩住宅小区分布	0.6949	双因子增强
5	公司企业分布∩距BRT站距离	0.6979	双因子增强	5	住宅小区分布∩距BRT站距离	0.6846	双因子增强
6	餐饮设施分布∩住宅小区分布	0.6923	双因子增强	6	距河流距离∩住宅小区分布	0.6779	双因子增强
7	餐饮设施分布∩公司企业分布	0.6860	双因子增强	7	住宅小区分布∩时均气温	0.6664	双因子增强
8	路网密度∩住宅小区分布	0.6672	双因子增强	8	路网密度∩住宅小区分布	0.6618	双因子增强
9	购物设施分布∩住宅小区分布	0.6653	双因子增强	9	餐饮设施分布∩公司企业分布	0.6613	双因子增强
10	距地铁站出口距离∩公司企业分布	0.6614	双因子增强	10	路网密度∩购物设施分布	0.6579	双因子增强

周一18时（到达量最大的晚高峰）				周四18时（到达量最小的晚高峰）
排序	主导交互因子	q值	交互结果	排序	主导交互因子	q值	交互结果
1	餐饮设施分布∩住宅小区分布	0.7804	双因子增强	1	餐饮设施分布∩住宅小区分布	0.7714	双因子增强
2	公司企业分布∩住宅小区分布	0.7739	双因子增强	2	公司企业分布∩住宅小区分布	0.7600	双因子增强
3	购物设施分布∩住宅小区分布	0.7664	双因子增强	3	购物设施分布∩住宅小区分布	0.7543	双因子增强
4	住宅小区分布∩距BRT站距离	0.7590	双因子增强	4	路网密度∩餐饮设施分布	0.7499	双因子增强
5	路网密度∩餐饮设施分布	0.7549	双因子增强	5	住宅小区分布∩距BRT站距离	0.7404	双因子增强
6	住宅小区分布∩时均气温	0.7397	双因子增强	6	住宅小区分布∩时均气温	0.7205	双因子增强
7	路网密度∩住宅小区分布	0.7341	双因子增强	7	距河流距离∩住宅小区分布	0.7095	双因子增强
8	距河流距离∩住宅小区分布	0.7324	双因子增强	8	路网密度∩住宅小区分布	0.7087	双因子增强
9	距地铁站出口距离∩住宅小区分布	0.7306	双因子增强	9	距地铁站出口距离∩住宅小区分布	0.7072	双因子增强
10	高程∩住宅小区分布	0.7164	双因子增强	10	路网密度∩购物设施分布	0.6949	双因子增强

广州市主城区共享单车骑行目的地时空特征与影响因素

Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city

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