公共交通与私家车出行的通勤效率差异及影响因素——以北京都市区为例

doi:10.11821/dlyj201702005

摘要/Abstract

摘要：

居住与就业是城市的基本功能活动,其空间配置决定了居民的通勤行为,从而对城市发展和居民生活产生显著影响,因此开展城市的通勤效率研究对优化居住与就业的空间分布,实现城市可持续发展等方面尤为重要。基于北京都市区居民问卷调查数据,通过理论通勤、过剩通勤、通勤容量等相关模型对居民的通勤效率进行评价;在此基础上,着重分析了公共交通出行与私家车出行之间的通勤效率差异性。研究表明：① 北京都市区居民的通勤出行中有64.48%属于过剩通勤,从通勤容量使用率来看都市区仅为32.49%,反映了目前居住与就业失衡的现实情况;② 从不同出行方式来看,公共交通过剩通勤程度更高,即私家车出行的通勤效率要高于公共交通,表明公共交通出行依然存在较大的优化提升空间;③ 从通勤效率差异的影响因素来看,就业可达性变量显著影响公共交通通勤,对私家车通勤时间的影响没有通过显著性检验;年龄、学历、平均月收入、住房产权等与二者显著相关,但性别、家庭结构的影响都不显著;居住密度及居住地空间位置对公共交通、私家车通勤时间都具有一定影响,但就业密度及就业地空间位置对不同出行方式通勤时间的影响则不显著,认为城市应当鼓励公共交通出行,通过提高公共交通的通达性、对私家车征收拥挤费用等措施来弥补公共交通与私家车出行之间的不平等性。

关键词: 通勤效率, 公共交通出行, 私家车出行, 北京都市区

Abstract:

Two main functions of a city—housing and employment—have a substantial influence on urban development, as their spatial configurations determine residents' commuting behavior and otherwise affect lifestyles. Research on commuting efficiency therefore plays an important role in urban sustainable development and optimism of the spatial distributions of housing and employment. In this paper, based on results of a questionnaire conducted in the Beijing metropolitan area, commuting efficiency is analyzed and evaluated by way of theoretical commuting, excess commuting, and commuting capacity models. The paper analyzes the commuting efficiency differentiation of public transit and private automobiles. Results are as follows. (1) The excess commuting rate in the Beijing metropolitan area is 64.48% while the commuting capacity utilization rate is only 32.49%, which permits speculation that the current employment-housing situation is substantially imbalanced. (2) For commuting efficiency for different travel modes, private automobiles are superior to public transit, which indicates that the public transportation excess commuting rate is higher than that of private automobiles, and great capacity exists for optimizing public transportation. (3) By analyzing impact factors of the commuting efficiency differentiation of public transit and private automobiles, the factor of job accessibility is found to significantly influence commuting via public transit, while the correlation between job accessibility and private automobile travel does not prove significant. A significant correlation is found between age, education, occupation type, average monthly income, housing property, and commuting time for both travel modes, but the influences of sex and family structure are found to be not significant. Additionally, residential locations are found to impact commuting times for both travel modes more than residential density does, whereas the impact of working locations is found to be not significant. Consequently, it is concluded that the urban government should encourage use of public transit. Furthermore, potentially effective measures such as improving availability of public transportation and imposing congestion-related premiums should be adopted to narrow the difference between public transit and private automobile use.

Key words: commuting efficiency, public transit travel, private automobile travel, Beijing metropolitan area

韩会然, 杨成凤, 宋金平. 公共交通与私家车出行的通勤效率差异及影响因素——以北京都市区为例[J]. 地理研究, 2017, 36(2): 253-266.

Huiran HAN, Chengfeng YANG, Jinping SONG. Impact factors and differences in commuting efficiencybetween public transit and private automobile travel: A case study on the Beijing metropolitan area[J]. GEOGRAPHICAL RESEARCH, 2017, 36(2): 253-266.

图/表 8

图1

表1

图2

图3

表2

图4

表3

回归方程变量属性特征"

变量	变量类型	描述
因变量
私家车出行的通勤时间Timecar	连续变量	乘坐私家车上班的单程时间
公共交通出行的通勤时间Timepub	连续变量	乘坐公共交通上班的单程时间
解释变量
1. 就业可达性变量
私家车出行的就业可达性JAcar	连续变量	依赖私家车上班的居民可选择的就业机会
公共交通出行的就业可达性JApub	连续变量	依赖公共交通上班的居民可选择的就业机会
职住比率JH	连续变量	所在单元的职住平衡程度
2. 社会经济变量
性别sex	虚拟变量	1：男;0：女
年龄age	等级变量	1：30岁以下;2：30~40岁;3：41~50岁; 4：51~60岁;5：60岁以上
学历education	等级变量	1：初中及以下;2：高中;3：大专;4：大学; 5：研究生及以上
职业类型occupation	等级变量	1-9分别表示政府工作人员、企业管理人员、专业技术人员、教师等
平均月收入income	等级变量	1：2000元以下;2：2000~3999元;3：4000~5999元; 4：6000~7999元;5：8000~10000元;6：10000元以上
家庭结构family	等级变量	1：单身独住;2：单身和父母同住;3：夫妻独住; 4：夫妻和父母同住;5：夫妻携子女居住; 6：三代以上同住;7：其他
3. 邻里特征变量
所在单元的就业密度jobdens	连续变量	居民所在街道的就业密度
所在单元的居住密度popudens	连续变量	居民所在街道的人口密度
4. 通勤成本变量
通勤花费costs	连续变量	居民上下班的交通费用
是否报销通勤费用baoxiao	虚拟变量	1：是;0：否
5. 空间属性变量
居住位置resilocation	等级变量	1：居住在中心城区;2：居住在近郊区;3：居住在远郊区
就业位置 joblocation	等级变量	1：在中心城区上班;2：在近郊区上班;3：在远郊区上班

表3

表4

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要素	分类	样本数	百分比(%)	要素	分类	样本数	百分比(%)
性别	男	842	42.8	年龄	30岁以下	762	38.7
性别	女	1125	57.2		31~40岁	631	32.1
职业	政府工作人员	112	5.7		41~50岁	538	27.4
	企事业管理人员	367	18.7	51~60岁		29	1.5
	专业技术人员	415	21.1	61岁以上		7	0.4
	商业、服务人员	145	7.4	受教育程度	初中及以下	92	4.7
	公司职员	504	25.6		高中及中专	134	6.8
	教师	134	6.8		大专	389	19.8
	个体户	72	3.7		本科	1022	52.0
	农民	20	1.0		研究生及以上	330	16.8
	其他	198	10.1	家庭构成	单身独住	345	17.5
个人月收入	2000元以下	94	4.8	家庭构成	单身和父母同住	212	10.8
	2000~3999元	432	22.0	夫妻独住		270	13.7
	4000~5999元	569	28.9	夫妻和父母同住		131	6.7
	6000~7999元	340	17.3	夫妻携子女		716	36.5
	8000~10000元	257	13.1	三代（含）以上同住		210	10.7
	10000元以上	275	14.0	其他		83	4.2

	Z_obs(km)	Z_min(km)	Z_max(km)	EC(%)	CU(%)
公共交通	14.25	3.33	22.35	76.60	57.41
私家车	12.51	4.14	24.02	66.80	42.10
北京都市区	11.81	4.19	27.64	64.48	32.49

变量	Timepub		Timecar
变量	系数	P值	系数	P值
Constant	25.859(3.06)	0.002	30.381(2.99)	0.003
JA	1.271(1.71)	0.087	-0.516(-0.38)	0.705
JA²	-0.142(-1.02)	0.006	-0.076(-0.66)	0.048
JH	-7.821(-3.04)	0.002	-1.366(-0.45)	0.050
JH²	-0.604(-1.92)	0.055	-0.327(-0.72)	0.471
sex	1.399(0.92)	0.360	0.677(0.40)	0.688
age	2.836(2.67)	0.008	-0.302(-0.23)	0.018
education	-2.238(-2.35)	0.019	0.264(0.26)	0.094
occupation	0.537(1.96)	0.042	-0.647(-1.15)	0.093
income	-1.189(-1.96)	0.650	0.882(1.39)	0.009
family	0.346(0.77)	0.443	-0.298(-0.41)	0.681
jobdens	-0.004(-0.66)	0.547	-0.002(-0.97)	0.443
popudens	-0.013(-0.71)	0.039	0.006(1.60)	0.001
costs	0.011(1.91)	0.257	0.163(0.25)	0.044
baoxiao	-0.195(-0.12)	0.904	0.605(0.34)	0.733
resilocation	6.134(3.62)	0.000	-0.518(-0.24)	0.011
joblocation	-2.481(-1.61)	0.107	1.101(0.78)	0.437
样本数量	852		348
R-squared	0.289		0.329
Adjusted R-squared	0.257		0.216
Log likelihood	-3763.829		-1420.350