建成环境对北京市郊区居民工作日汽车出行的影响

doi:10.11821/dlxb201510011

Abstract

Abstract:

Car use has changed daily activity travel patterns, which cause serious urban problems including air pollution, traffic congestion, road accidents, and community severance. Particularly in China's rapid suburbanization, car use and related social and environmental issues are attracting great attention. Previous research has illustrated the importance of the built environment and car ownership on daily car travel distance. However, it is still not clear how car ownership and car use impact individual behavior, and the ways researchers measure the contextual influence of the built environment are not consistent. Most of the existing literature only uses residential area as the geographic context to study the impact of built environment, and only a few studies focus on workplaces or other destinations. In recent years, the uncertain geographic context problem has come under intense scrutiny by geographers seeking to elucidate the interaction between urban space and individual behavior. According to this phenomenon of the uncertain geographic context, travel behavior is influenced not only by the origin and destinations of trips, but also by the travel routes and the surrounding activity spaces. However, so far few studies have been conducted on the impact of activity space. Based on a GPS-facilitated activity-travel survey dataset collected in the Shangdi-Qinghe area in Beijing in 2012, the present paper studies the relationship between the built environment and car travel behavior of suburban residents on weekdays. To understand the importance of geographic context, three types of geographic context are used: residential area, work location and activity space. The impact of the built environment on car travel distance in daily travel, commuting travel and non-work travel is analyzed using three sets in a linear regression model. The study finds that the impact of the built environment on car travel behavior depends on travel mode and geographic context, and the built environment in work locations and activity spaces has a larger impact on car travel distance than that in residential areas. The built environment in residential areas has an influence on daily travel distance, but lower development density and better residential accessibility in residential areas can reduce car travel distance in commuting. Higher development density in both work locations and activity spaces is related to longer car travel distance in daily life, commuting and non-work travel behavior. Contrary to the findings of Western studies, better accessibility to public transit leads to more car use in non-work travel. This paper discusses the importance of the uncertain geographic context problem, indicating that activity space plays an important role in daily activity travel behavior. These findings have key implications for policy-making decision. The built environment should be a great concern in policies aimed at car use limitation.

Key words: geographic context, built environment, car travel, suburbs, Beijing

na Ta, Yanwei CHAI, Mei-Po KWAN. The relationship between the built environment and car travel distance on weekdays in Beijing[J].Acta Geographica Sinica, 2015, 70(10): 1675-1685.

Figures/Tables 6

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变量		样本数	频率(%)
性别	男性	140	47.78
性别	女性	153	52.22
年龄		36.17
户口	非北京户口	39	13.31
户口	北京户口	254	86.69
个人月收入	低于2000元	32	10.92
	2001~6000元	180	61.43
	6001元以上	81	27.65
教育程度	高中及以下	42	14.33
	大专或大学	199	67.92
	研究生及以上	52	17.75
住房产权	自有住房	254	86.69
住房产权	租住住房	39	13.31
汽车所有权	有小汽车	185	63.14
汽车所有权	无小汽车	108	36.86
驾照	有驾照	142	48.56
驾照	无驾照	151	51.54
家庭规模	1	5	1.71
	2	68	23.21
	3	141	48.12
	4+	79	26.96

	模型1：居住地属性			模型2：居住地+工作地			模型3：居住地+活动空间
	系数		P值		系数	P值	系数	P值
女性	-6.95	0.001		-6.88	0.001		-6.16	0.002
年龄	-0.29	0.034		-0.31	0.024		-0.30	0.024
户口	-3.03	0.283		-3.34	0.238		-2.93	0.292
驾照	10.92	0.000		9.89	0.000		9.24	0.000
个人收入	-1.03	0.261		-0.66	0.479		-0.57	0.533
教育水平	1.81	0.332		1.70	0.358		1.63	0.371
住房所有权	3.57	0.215		3.99	0.171		3.05	0.280
家庭规模	-1.75	0.168		-1.80	0.156		-1.38	0.269
汽车所有权	6.09	0.009		6.79	0.004		5.67	0.013
居住地公交密度	0.10	0.363		0.13	0.245		0.12	0.275
居住地零售商业密度	-0.06	0.772		0.03	0.890		-0.08	0.695
居住地停车场密度	-0.04	0.866		-0.12	0.615		-0.16	0.547
居住地建设密度	0.02	0.490		0.02	0.494		0.03	0.305
工作地公交站密度				0.13	0.160
工作地零售商业密度				0.10	0.403
工作地停车场密度				-0.03	0.855
工作地建设密度				-0.03	0.007
活动空间公交站密度							-0.09	0.795
活动空间零售商业密度							0.44	0.405
活动空间停车场密度							0.31	0.640
活动空间建设密度							-0.11	0.002
截距	17.52	0.061		16.47	0.082		27.05	0.011
R²	0.2567			0.2839			0.3055

	模型1：居住地属性			模型2：居住地+工作地			模型3：居住地+活动空间
	系数		P值		系数	P值	系数	P值
女性	-4.04	0.001		-3.83	0.003		-3.77	0.003
年龄	-0.09	0.298		-0.10	0.275		-0.10	0.241
户口	0.21	0.906		-0.05	0.980		0.11	0.950
驾照	5.07	0.000		4.67	0.002		4.46	0.002
个人收入	-0.55	0.348		-0.32	0.592		-0.42	0.473
教育水平	-0.12	0.926		-0.10	0.936		-0.06	0.962
住房所有权	2.25	0.220		2.10	0.264		1.92	0.292
家庭规模	-0.29	0.718		-0.18	0.820		-0.10	0.904
汽车所有权	2.24	0.132		2.46	0.105		2.13	0.149
居住地公交密度	0.00	0.970		0.01	0.841		0.03	0.686
居住地零售商业密度	-0.26	0.042		-0.23	0.082		-0.30	0.026
居住地停车场密度	-0.02	0.876		-0.06	0.699		-0.05	0.769
居住地建设密度	0.04	0.012		0.04	0.011		0.05	0.006
工作地公交站密度				0.04	0.471
工作地零售商业密度				0.04	0.603
工作地停车场密度				0.00	0.983
工作地建设密度				-0.01	0.018
活动空间公交站密度							-0.21	0.327
活动空间零售商业密度							0.30	0.409
活动空间停车场密度							0.14	0.748
活动空间建设密度							-0.05	0.064
截距	6.08	0.309		5.63	0.359		12.11	0.074
R²	0.195			0.2162			0.2285

	模型1：居住地属性			模型2：居住地+工作地			模型3：居住地+活动空间
	系数		P值		系数	P值	系数	P值
女性	-5.18	0.006		-5.66	0.003		-4.73	0.011
年龄	-0.14	0.252		-0.15	0.238		-0.10	0.430
户口	-6.72	0.012		-6.80	0.012		-6.55	0.013
驾照	4.31	0.040		3.63	0.092		4.06	0.049
个人收入	-1.44	0.116		-1.38	0.129		-1.49	0.098
教育水平	2.48	0.158		3.03	0.086		3.28	0.062
住房所有权	2.74	0.311		3.19	0.254		2.02	0.451
家庭规模	-0.78	0.508		-0.83	0.482		-0.84	0.467
汽车所有权	4.66	0.036		4.92	0.030		4.21	0.054
居住地公交站密度	0.22	0.032		0.23	0.020		0.19	0.069
居住地零售商业密度	0.31	0.121		0.35	0.080		0.26	0.216
居住地停车场密度	-0.08	0.703		-0.17	0.443		-0.11	0.645
居住地建设密度	-0.04	0.127		-0.05	0.103		-0.04	0.122
工作地公交站密度				0.14	0.089
工作地零售商业密度				0.13	0.282
工作地停车场密度				0.08	0.600
工作地建设密度				-0.02	0.070
活动空间公交站密度							0.72	0.033
活动空间零售商业密度							0.71	0.189
活动空间停车场密度							-0.26	0.687
活动空间建设密度							-0.10	0.005
截距	15.50	0.086		12.35	0.181		11.15	0.253
R²	0.2806			0.324			0.329

The relationship between the built environment and car travel distance on weekdays in Beijing

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