Will Automated Vehicles Drive You to Move? Exploring and Predicting the Impact of AV Technology on Residential Relocation

Song Wang, Xin Tian, Zhixia Li (), Shang Jiang, Wenjing Zhao, Shiyao Zhang, Hao (Frank) Yang and Guohui Zhang
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Song Wang: Department of Traffic and Transportation Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Xin Tian: Department of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Zhixia Li: Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45221, USA
Shang Jiang: Department of Civil and Natural Resource Engineering, University of Canterbury, Christchurch 8041, New Zealand
Wenjing Zhao: Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
Shiyao Zhang: School of Advanced Engineering, Great Bay University and Great Bay Institute for Advanced Study (GBIAS), Dongguan 523000, China
Hao (Frank) Yang: Department of Civil and Systems Engineering, Whiting School of Engineering, John Hopkins University, Baltimore, MD 21218, USA
Guohui Zhang: Department of Environmental and Construction Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA

Sustainability, 2025, vol. 17, issue 21, 1-31

Abstract: Automated vehicle (AV) technology is expected to alter travel behavior and residential location choices, yet the psychological motivations behind relocation decisions under current partial automation (Level 2) remain underexplored, as most studies focus on fully autonomous scenarios. This study explores why individuals might relocate in response to AV availability in both short-term and long-term contexts and predicts how willingness to relocate changes as automation levels advance. In a survey of Kentucky residents, data were collected on demographic and economic characteristics, travel needs, built environment attributes, AV familiarity, comfort with different automation levels, and willingness to relocate if AVs were available. Multiple machine learning models with Shapley Additive Explanations (SHAP) were used to predict and interpret changes in relocation willingness. Results indicate that greater comfort with high-level automation and higher AV familiarity increase relocation intentions, particularly among men, older adults with higher incomes, and urban residents. SHAP analysis reveals that built environment, age, and comfort with fully autonomous driving are the most influential predictors of changes in relocation willingness. Findings inform land use and housing policy by identifying where perception-driven relocation pressures are likely to emerge and by outlining adaptive tools to guide spatial growth as AV technology advances.

Keywords: automated vehicle; residential relocation; machine learning; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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