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Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice
被引:115
|作者:
Ding, Chuan
[1
]
Wang, Yunpeng
[1
]
Tang, Tieqiao
[1
]
Mishra, Sabyasachee
[2
]
Liu, Chao
[3
]
机构:
[1] Beihang Univ, Beijing Key Lab Cooperat Vehicle Infrastruct Syst, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China
[2] Univ Memphis, Dept Civil Engn, Memphis, TN 38152 USA
[3] Univ Maryland, Natl Ctr Smart Growth Res, Fac Res Associate, College Pk, MD 20742 USA
基金:
中国国家自然科学基金;
中国博士后科学基金;
关键词:
Car ownership;
Travel mode choice;
Built environment;
Spatial heterogeneity;
Mediating effect;
RESIDENTIAL SELF-SELECTION;
LAND-USE;
AUTO OWNERSHIP;
NEIGHBORHOOD CHARACTERISTICS;
URBAN FORM;
BEHAVIOR;
AREAS;
CONNECTION;
CALIFORNIA;
DECISIONS;
D O I:
10.1016/j.trd.2016.08.004
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter's driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures. (C) 2016 Elsevier Ltd. All rights reserved.
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页码:28 / 40
页数:13
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