Accommodating Immigration Status and Self-Selection Effects in a Joint Model of Household Auto Ownership and Residential Location Choice

被引:28
作者
Bhat, Chandra R. [1 ,2 ]
Paleti, Rajesh [3 ]
Pendyala, Ram M. [4 ]
Lorenzini, Karen [5 ]
Konduri, Karthik C. [6 ]
机构
[1] Univ Texas Austin, Dept Civil Architectural & Environm Engn, Austin, TX 78712 USA
[2] King Abdulaziz Univ, Jeddah 21589, Saudi Arabia
[3] Parsons Brinckerhoff, New York, NY 10119 USA
[4] Arizona State Univ, Sch Sustainable Engn & Built Environm, Tempe, AZ 85287 USA
[5] Texas A&M Univ, Texas A&M Transportat Inst, Austin Off, Austin, TX 78723 USA
[6] Univ Connecticut, Dept Civil & Environm Engn, Unit 3037, Storrs, CT 06269 USA
关键词
TRAVEL BEHAVIOR; BUILT ENVIRONMENT; IMPACT;
D O I
10.3141/2382-16
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
As the proportion of immigrants in the U.S. population continues to rise, it is becoming increasingly important to understand immigrants' residential location choices and travel behavior in the travel modeling and transportation policy making arena. This paper presents a joint model of choice of residential location and auto ownership that explicitly accounts for immigration status and length of stay in the United States as explanatory variables. Results of model estimation from the use of a San Francisco, California, Bay Area subsample of the 2009 National Household Travel Survey show that immigration and length of stay are significant explanatory variables in both choice of residential location and auto ownership, with immigrants displaying assimilation effects (i.e., they increasingly resemble nonimmigrant households as the length of stay increases). Even after immigration effects have been controlled and residential location has been included as an explanatory variable in the auto ownership model, significant self-selection effects are likely to dampen estimates of the impacts of land use changes on travel behavior in policy forecasts. The paper demonstrates the need to account for immigration variables and self-selection effects in transportation forecasting models that inform policy decisions.
引用
收藏
页码:142 / 150
页数:9
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