波士顿共享单车出行与街区建成环境的关系研究

doi:10.3969/j.issn.1004-9479.2023.02.2021256

摘要/Abstract

摘要：

共享单车作为低碳环保的交通出行方式，在全球许多城市得到了快速发展。但已有共享单车与建成环境的关系研究较少关注街区空间尺度及形态因素，且忽略了影响因素的非线性作用。基于波士顿共享单车出行数据，分析共享单车出行的时空特征；运用广义可加混合模型，解析骑行量与街区建成环境的关系。结果表明：①波士顿共享单车骑行量由中心区向城市外围呈圈层梯度递减趋势，骑行距离具有3km集中骑行圈与6km极限骑行圈特征；②周末、天气、温度等时变因素会显著影响单车使用；③骑行量与街区总人口数、路网密度和地铁接入性显著正相关，与街区路网方向熵、平均路段长度分别呈L形、倒U形的非线性关系。最后，从可骑行社区构建、TOD慢行系统建设以及精准化共享单车投放等方面，阐述了研究对我国城市的启示。

关键词: 共享单车, 时空特征, 建成环境, 广义可加混合模型, 波士顿, 启示

Abstract:

As a low-carbon and environmentally friendly travel mode, bike-sharing (BS) has developed rapidly in many cities at home and abroad. However, existing studies have paid little attention to the relationship between urban morphology factors and BS ridership, and overlooked the nonlinearity of the relationship. This study analyzes the spatio-temporal characteristics of BS trips based on Boston's BS system data. We use generalized additive mixed model (GAMM) to capture the nonlinear relationship between BS ridership and built environment characteristics. The results show that: (1) BS ridership has a spatial pattern of attenuation from the downtown area to the periphery area. BS travel distances are concentrated at 3km, with a limit of 6km. (2) Temperature, weekday, and weather have significant effects on BS ridership. (3) BS ridership is linearly and positively correlated with total population, road density and metro accessibility, and has a nonlinear relationship with street orientation entropy and average road length. Finally, we provide corresponding policy implications for China based on the empirical findings.

Key words: bike-sharing, spatio-temporal characteristics, built environment, generalized additive mixed model, Boston, implications

喻冰洁, 梁源, 杨林川. 波士顿共享单车出行与街区建成环境的关系研究[J]. 世界地理研究, 2023, 32(2): 48-58.

Bingjie YU, Yuan LIANG, Linchuan YANG. Exploring the relationship between bike-sharing ridership and built environment characteristics: A case study based on GAMM in Boston[J]. World Regional Studies, 2023, 32(2): 48-58.

图/表 7

图1 波士顿共享单车系统站点分布图

Fig.1 Spatial distribution of Blue Bikes' stations in Boston

表1 变量描述性统计

Tab.1 Descriptive statistics of variables

变量		描述	平均值	标准差	最小值	最大值
站点特征	站点日出行量	从该站点出发的订单数量	36.81	20.86	0.00	372.00
站点特征	站点停车总容量	每个站点的总泊位数量	17.76	4.40	3.00	47.00
建成环境指标	人口数量	单位：1000人	1.29	0.64	0.00	3.48
	距城市CBD的距离	单位：1000m	5.82	3.18	0.19	14.44
	职住混合指数	1=最混合，0=最单一	0.62	0.22	0.00	0.96
	距最近公交站距离	单位：100m	2.09	1.25	0.14	7.15
	地铁接入性	站点500m范围内有地铁站（1=有，0=无）	0.48	—	—	—
	路网密度	单位：km/km²	20.33	2.24	12.55	28.03
	平均路段长度	单位：m	68.36	6.89	45.89	91.55
	路网方向熵	越小代表街道方向越规整	3.34	0.17	2.49	3.55
时变因素	周末	1=周末，0=工作日	0.28	—	—	—
	天气	1=雨天，0=晴天	0.27	—	—	—
	平均温度	单位：摄氏度	22.41	3.65	12.00	30.50

图2 共享单车日均骑行量时空分布变化图

Fig.2 The spatial distribution of station-level average daily ridership

图3 共享单车日骑行量分布图

Fig.3 The total daily ridership during the study period

图4 共享单车出行距离概率密度分布图

Fig.4 The probability density distribution of travel distance

表2 模型结果 (Model results)

Tab.2

参数	解释变量	系数估计值	标准误	Z值	p值
参数项	截距项	2.902	0.071	41.077	<0.001
	周末	-0.105	0.003	-40.201	<0.001
	天气	-0.207	0.002	-84.539	<0.001
	地铁接入性	0.470	0.111	4.279	<0.001
参数	解释变量	估计自由度	参考自由度	卡方检验量	p值
非参数平滑项	时间趋势项	8.828	8.991	4102.000	<0.001
	平均温度	8.957	8.999	1780.000	<0.001
	站点停车总容量	2.587	2.588	38.740	<0.001
	人口数量	1.000	1.000	4.969	0.026
	职住混合指数	1.000	1.000	1.717	0.190
	距最近公交站距离	1.000	1.000	0.464	0.496
	距城市CBD距离	1.000	1.000	33.880	<0.001
	路网密度	1.000	1.000	7.713	0.005
	街道平均长度	3.911	3.915	19.780	0.001
	路网方向熵	4.958	4.961	14.720	0.011
调整后 $R 2$		0.878
解释的偏差		90%
样本量		26 312

表2 模型结果 (Model results)

Tab.2

参数	解释变量	系数估计值	标准误	Z值	p值
参数项	截距项	2.902	0.071	41.077	<0.001
	周末	-0.105	0.003	-40.201	<0.001
	天气	-0.207	0.002	-84.539	<0.001
	地铁接入性	0.470	0.111	4.279	<0.001
参数	解释变量	估计自由度	参考自由度	卡方检验量	p值
非参数平滑项	时间趋势项	8.828	8.991	4102.000	<0.001
	平均温度	8.957	8.999	1780.000	<0.001
	站点停车总容量	2.587	2.588	38.740	<0.001
	人口数量	1.000	1.000	4.969	0.026
	职住混合指数	1.000	1.000	1.717	0.190
	距最近公交站距离	1.000	1.000	0.464	0.496
	距城市CBD距离	1.000	1.000	33.880	<0.001
	路网密度	1.000	1.000	7.713	0.005
	街道平均长度	3.911	3.915	19.780	0.001
	路网方向熵	4.958	4.961	14.720	0.011
调整后 $R 2$		0.878
解释的偏差		90%
样本量		26 312

图5 骑行量与解释变量的关系（灰色阴影为95%置信区间）

Fig.5 The relationship between the dependent variable and explanatory variables（grey shades refer to 95% CI）

参考文献 33

1	SUN S. The spatial spread of dockless bike-sharing programs among Chinese cities. Journal of Transport Geography, 2020, 86: 102782.
2	曹小曙,罗依.中国大陆城市建成环境与共享单车配置的关系.中山大学学报(自然科学版),2020,59(1):77-85.
	CAO X, LUO Y. Relationship between built environment and bike share allocation in the mainland of China. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2020, 59(1):77-85.
3	BONGIORNO C, SANTUCCI D, KON F, et al. Comparing bicycling and pedestrian mobility: Patterns of non-motorized human mobility in Greater Boston. Journal of Transport Geography, 2019, 80: 102501.
4	林堉楠,徐媛,杨家文.轨道交通车站周边建成环境对骑行的影响——基于深圳市ofo数据的实证研究.城市交通,2020,18(1):83-94.
	LIN Y, XU Y, YANG J. Built environment on linking bicycle to rail transit: Case study based on ofo data in Shenzhen. Urban Transport of China, 2020, 18(1):83-94.
5	马新卫,季彦婕,金雪,等.租赁自行车用户出行特征及方式的影响因素分析.浙江大学学报(工学版), 2020, 54(6):1202-1209.
	MA X, JI Y, JIN X, et al. Analysis on travel characteristics of bike-sharing users and influence factors on way to travel. Journal of Zhejiang University (Engineering Science) 2020, 54(6):1202-1209.
6	CHEVALIER A, CHARLEMAGNE M, XU L. Bicycle acceptance on campus: Influence of the built environment and shared bikes. Transportation Research Part D: Transport and Environment, 2019, 76: 211-235.
7	AHILLEN M, MATEO-BABIANO D, CORCORAN J.Dynamics of bike sharing in Washington,DC and Brisbane, Australia: Implications for policy and planning.International Journal of Sustainable Transportation,2016,10(5): 441-454.
8	孙艺玲,仝德,曹超.城市建成环境对公共自行车使用的影响机制研究—以深圳市南山区为例.北京大学学报(自然科学版),2018,54(6):1325-1331.
	SUN Y, TONG D, CAO C.How urban built environment affects the use of public bicycles: A case study of Nanshan District of Shenzhen.Acta ScientiarumNaturalium Universitatis Pekinensis,2018,54(6): 1325-1331.
9	El-ASSI W, MAHMOUD M, HABIB K. Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 2017,44(3):589-613.
10	高枫,李少英,吴志峰,等.广州市主城区共享单车骑行目的地时空特征与影响因素.地理研究,2019,38(12):2859-2872.
	GAO F, LI S, WU Z,et al.Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou City. Geographical Research,2019,38(12):2859-2872.
11	CORCORAN J, LI T, ROHDE D, et al. Spatio-temporal patterns of a public bicycle sharing program: The effect of weather and calendar events. Journal of Transport Geography, 2014, 41: 292-305.
12	FAGHIH-IMANI A, ELURU N, EL-GENEIDY A, et al. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 2014, 41: 306-314.
13	BÖCKER L, ANDERSON E, UTENG T,et al.Bike sharing use in conjunction to public transport: Exploring spatiotemporal, age and gender dimensions in Oslo, Norway.Transportation Research Part A Policy and Practice, 2020, 138: 389-401.
14	马新卫,季彦婕,金雨川,等.基于时空地理加权回归的共享单车需求影响因素分析.吉林大学学报(工学版),2020,50(4):1344-1354.
	MA X, JI Y, JIN Y, et al. Geographically and temporally weighted regression for modeling spatiotemporal variation in dockless bikeshare usage demand. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(4): 1344-1354.
15	罗桑扎西,甄峰,尹秋怡.城市公共自行车使用与建成环境的关系研究——以南京市桥北片区为例.地理科学,2018,38(3):332-341.
	LUO S, ZHEN F, YIN Q. How built environment influence public bicycle usage: Evidence from the bicycle sharing system in Qiaobei Area, Nanjing.Scientia Geographica Sinica,2018,38(3):332-341.
16	WANG K, CHEN Y. Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions. Journal of Transport Geography, 2020, 82: 102603.
17	KUO Y, HSIEH C, HUNG Y. Non-linear characteristics in switching intention to use a docked bike-sharing system. Transportation, 2020, 48(3): 1459-1479.
18	DING C, CAO X, NAESS P. Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo. Transportation Research Part A: Policy and Practice,2018, 110: 107-117.
19	TAO T, WANG J, CAO X. Exploring the non-linear associations between spatial attributes and walking distance to transit. Journal of Transport Geography, 2020, 85:102560.
20	QIAN L, CHUAN D, PENG C. A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand. Travel Behaviour and Society, 2020, 18:29-36.
21	HE P, ZOU Z, ZHANG Y, et al. Boosting the eco-friendly sharing economy: The effect of gasoline prices on bikeshare ridership in three US metropolises. Environmental Research Letters, 2020, 15(11): 114021.
22	RAMSEY K, BELL A. The smart location database: A nationwide data resource characterizing the built environment and destination accessibility at the neighborhood scale. Cityscape, 2014, 16(2): 145-162.
23	GRIFFIN G, JIAO J. Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus. Journal of Transport & Health, 2015, 2(2): 238-247.
24	BOEING G. Urban spatial order: Street network orientation, configuration, and entropy. Social Science Electronic Publishing, 2019, 4(1): 67.
25	XIE F, LEVINSON D. Measuring the structure of road networks. Geographical Analysis, 2007, 39(3): 336-356.
26	WOOD S, GOUDE Y, SHAW S. Generalized Additive Models for large data sets. Journal of the Royal Statistical Society C: Applied Statistics, 2015, 64(1): 139-155.
27	LIN X, ZHANG D. Inference in Generalized Additive Mixed Models by using smoothing splines. Journal of the royal statistical society: Series b (statistical methodology), 1999, 61(2): 381-400.
28	FOURNIER N, CHRISTOFA E, KNODLER J.A Sinusoidal Model for seasonal bicycle demand estimation. Transportation Research Part D: Transport and Environment, 2017, 50: 154-169.
29	RUDLOFF C, LACKNER B. Modeling demand for bikesharing systems: Neighboring stations as source for demand and reason for structural breaks. Transportation Research Record, 2014, 2430(1): 1-11.
30	王珏玉.共享单车与公共交通的关系:文献综述.上海城市规划,2020(5):41-45.
	WANG J. The relationship between bike sharing programs and public transit: A literature review.Shanghai Urban Planning Review, 2020(5): 41-45.
31	OWEN N, HUMPEL N, LESLIE E, et al. Understanding environmental influences on walking: Review and research agenda. American journal of preventive medicine, 2004, 27(1): 67-76.
32	OGILVIE D, MITCHELL R, MUTRIE N, et al. Personal and environmental correlates of active travel and physical activity in a deprived urban population. International Journal of Behavioral Nutrition and Physical Activity,2008,5(1):43.
33	陈菲,周素红,张琳.生命周期视角下建成环境对居民休闲体力活动的影响.世界地理研究,2019,28(5):106-117.
	CHEN F, ZHOU S, ZHANG L. Influence of built environment on residents' leisure timephysical activity from the perspective of life cycle. World Regional Studies, 2019, 28(5): 106-117.

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