基于出行链的建成环境对居民小汽车通勤出行的影响

doi:10.18306/dlkxjz.2021.04.011

地理科学进展 ›› 2021, Vol. 40 ›› Issue (4): 671-680.doi: 10.18306/dlkxjz.2021.04.011

基于出行链的建成环境对居民小汽车通勤出行的影响

张雪¹^,²(), 周素红¹^,²^,^*(), 陈菲³

1.中山大学地理科学与规划学院,广州 510275
2.广东省公共安全与灾害工程技术研究中心,广州 510275
3.中国城市规划设计研究院,广东深圳 518040

收稿日期:2020-06-17 修回日期:2020-10-19 出版日期:2021-04-28 发布日期:2021-06-28
通讯作者: *周素红(1976— ),女,广东饶平人,教授,博士生导师,研究方向为城市地理学、城市交通、时空间行为、城市政策与城乡规划。E-mail: eeszsh@mail.sysu.edu.cn
作者简介:张雪(1992— ),女,湖南娄底人,博士生,主要研究方向为时空行为、城市交通和健康地理。E-mail: zhangx396@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(71961137003);国家自然科学基金项目(41871148);广东省重点领域研发计划项目(2020B0202010002)

Impact of the built environment on residents’ car commuting based on trip chain

ZHANG Xue¹^,²(), ZHOU Suhong¹^,²^,^*(), CHEN Fei³

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
3. China Academy of Urban Planning & Design, Shenzhen 518040, Guangdong, China

Received:2020-06-17 Revised:2020-10-19 Online:2021-04-28 Published:2021-06-28
Supported by:
National Natural Science Foundation of China(71961137003);National Natural Science Foundation of China(41871148);Key Research and Development Program of Guangdong Province(2020B0202010002)

摘要/Abstract

摘要：

城市建成环境对居民出行,尤其是小汽车通勤出行的影响是交通需求预测的基础性命题,长期受到关注。已有研究大多以单次出行作为研究对象,较少关注出行链影响下的出行协同决策问题。论文基于2017年广州市居民活动日志调查数据,采用Logistic模型检验了出行链视角下城市建成环境对工作日选择小汽车通勤出行的影响。研究发现,出行链中的非通勤出行与职住地建成环境存在交互作用关系,共同影响居民小汽车通勤出行方式选择。一方面,非通勤出行的时间顺序(到达就业地前的前项出行还是离开就业地后的后项出行)会影响职住地建成环境对小汽车通勤出行方式选择的作用关系;另一方面,非通勤出行目的地的建成环境要素会调节职住地建成环境与小汽车出行方式选择的关系。研究结论可为个体交通行为预测和基于个体模型的交通需求预测提供新的思路。

关键词: 建成环境, 小汽车通勤出行, 出行链, 交互作用

Abstract:

The impact of urban built environment on residents' travel behavior, especially car commuting, is a basic topic of traffic demand forecasting, which has received considerable research attention. Existing studies mainly focused on single trips and paid less attention to the collaborative decision making under the influence of trip chains. By using the logistic model and detailed activity diary data collected in Guangzhou City in 2017, this study investigated the relationship between urban built environment and car commuting on weekdays from the trip chain perspective. Empirical results indicate that there is an interactive relationship between non-commuting trip in the trip chain and the built environment of the workplace and residence, which synergistically affects residents' commuting modes. On the one hand, the non-commuting trip time (before or after the commuter's trip) make difference in the relationship between the built environment and commuting mode choice. On the other hand, the mutual influence of several built environment factors—residential area, work location, and non-commuting activity—determines the commuting behavior. Therefore, the extension of the results may provide new thoughts for predicting individual traffic behavior and traffic demand based on individual models.

Key words: built environment, car commuting trip, trip chain, interaction

张雪, 周素红, 陈菲. 基于出行链的建成环境对居民小汽车通勤出行的影响[J]. 地理科学进展, 2021, 40(4): 671-680.

ZHANG Xue, ZHOU Suhong, CHEN Fei. Impact of the built environment on residents’ car commuting based on trip chain[J]. PROGRESS IN GEOGRAPHY, 2021, 40(4): 671-680.

图/表 5

图1

表1

表2

表3

工作日小汽车通勤出行方式选择的二元Logistic模型结果"

变量		未考虑其他活动出行	考虑其他活动出行
变量		模型1	模型2 (居住地)	模型3 (工作地)	模型4 (居住地和工作地)	模型5 (前项出行交互作用)	模型6 (后项出行交互作用)
常数项	-5.237^**	-5.610^***	-5.267^***	-5.322^***	-5.305^***	-8.454^***
社会经济属性
年龄	31~45岁	0.956^**	0.963^**	0.999^**	1.222^**	1.223^**	1.268^**
年龄	46~59岁	-0.421	-0.287	-0.313	-0.012	-0.082	-0.217
是否有小汽车	有	3.181^***	3.392^***	3.241^***	3.360^***	3.465 ^***	3.551^***
月收入	4001~8000元	1.872^**	1.602^**	1.811^**	1.356^*	1.374^*	1.525^*
月收入	>8000元	2.576^***	2.195^***	2.388^***	1.954^**	2.007 ^**	2.173^**
出行特性
有无出行同伴	有	2.250^***	2.163^***	2.292^***	2.036^***	2.402^***	2.507^***
出行时间	-1.926^***	-1.649^**	-1.910^***	-1.958^***	-2.187^***	-2.086^***
X						-3.021
Y							2.850
建成环境
H_土地利用混合度		-0.794^**		-0.721^**	-0.782^**	-0.196
H_建设密度		0.381		0.271	0.273	-2.690
H_交叉口密度		-0.192		-0.339	-0.061	-1.139
H_可达性		-0.089		-0.134	-0.402	3.157
W_土地利用混合度			-0.171	-0.193	-0.195	-0.503
W_建设密度			-0.205	0.128	0.041	1.430
W_交叉口密度			0.198	0.262	0.411	-4.609
W_可达性			0.069	0.041	0.219	3.052
H_土地利用混合度×X					-0.581
H_建设密度×X					1.925
H_交叉口密度×X					-5.322
H_可达性×X					2.540^*
W_土地利用混合度×X					-0.816
W_建设密度×X					1.175
W_交叉口密度×X					-1.440
W_可达性×X					-0.757
H_土地利用混合度×Y						-0.767
H_建设密度×Y						3.016
H_交叉口密度×Y						1.377
H_可达性×Y						-3.653
W_土地利用混合度×Y						0.170
W_建设密度×Y						-1.569
W_交叉口密度×Y						4.881
W_可达性×Y						-2.632
伪R²	0.529	0.571	0.539	0.583	0.621	0.619
-2 log likelihood	183.96	170.54	180.70	165.55	153.54	154.04
P	0.001	0.103	<0.001	0.014	<0.001	0.013

表3

表4

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变量		样本数	比例/%
性别	男性	489	49.80
性别	女性	493	50.20
年龄	19~30岁	384	39.10
	31~45岁	378	38.50
	46~59岁	220	22.40
教育	高中及以下	329	33.50
	大专或本科	644	65.58
	研究生	9	0.92
就业	全职就业	975	99.29
就业	半职就业	7	0.71
是否拥有小汽车	有	355	36.15
是否拥有小汽车	无	627	63.85
月收入	4000元及以下	205	20.88
	4001~8000元	580	59.06
	>8000元	197	20.06

出行链	总样本		使用小汽车通勤出行样本
出行链		数量/人	比例/%	数量/人	在总样本中占比/%
H→W→H	546	55.60	66	12.09
H→R→W→H	51	5.20	4	7.84
H→W→R→H	356	36.25	46	12.92
H→R→W→R→H	29	2.95	4	13.79
总计	982	100	120	12.22

居住地交互影响			工作地交互影响
变量		系数	变量		系数
常数项		-6.049^**	常数项		-5.306^***
年龄	31~45岁	0.945^*	年龄	31~45岁	1.259^**
年龄	46~59岁	-0.537	年龄	46~59岁	-0.323
是否有小汽车	有	3.508^***	是否有小汽车	有	3.272^***
月收入(元)	4001~8000元	1.831^**	月收入	4001~8000元	1.854^**
月收入(元)	>8000元	2.175^***	月收入	>8000元	2.215^***
有无出行同伴	有	2.666^***	有无出行同伴	有	2.639^***
出行时间		-1.532^*	出行时间		-2.166^***
H_土地利用混合度		-1.006^***	W_土地利用混合度		-0.245
H_建设密度		0.437	W_建设密度		0.166
H_交叉口密度		-0.385	W_交叉口密度		0.329
H_可达性		-0.184	W_可达性		0.054
L_土地利用混合度		-0.044	L_土地利用混合度		-0.021
L_建设密度		-0.012	L_建设密度		0.006
L_交叉口密度		0.184^*	L_交叉口密度		0.033
L_可达性		-0.065	L_可达性		0.007
H×L_土地利用混合度		0.022	W×L_土地利用混合度		-0.125
H×L_建设密度		-0.109	W×L_建设密度		-1.050^*
H×L_交叉口密度		0.677	W×L_交叉口密度		-0.447
H×L_可达性		0.011	W×L_可达性		0.756
伪R²		0.622	伪R²		0.592
-2 log likelihood		140.42	-2 log likelihood		149.60
Sig		0.011	Sig		<0.001

基于出行链的建成环境对居民小汽车通勤出行的影响

Impact of the built environment on residents’ car commuting based on trip chain

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