Will Automated Vehicles Drive You to Move? Exploring and Predicting the Impact of AV Technology on Residential Relocation
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.
Authors
- Hao Yang (ORCID: https://orcid.org/0000-0001-6431-8956)
- Guohui Zhang (ORCID: https://orcid.org/0000-0001-5194-9222)
- Shiyao Zhang (ORCID: https://orcid.org/0000-0002-0004-1801)
- Shang Jiang (ORCID: https://orcid.org/0000-0001-5325-7633)
- Zhixia Li (ORCID: https://orcid.org/0000-0002-7942-4660)
- Song Wang (ORCID: https://orcid.org/0000-0003-2768-728X)
- Wenjing Zhao (ORCID: https://orcid.org/0000-0003-4775-630X)
- Xin Tian (ORCID: https://orcid.org/0009-0007-9881-9549)
Institutions
- University of Hawaiʻi at Mānoa (US)
- Hong Kong Polytechnic University (HK)
- Johns Hopkins University (US)
- University of Canterbury (NZ)
- Johns Hopkins Medicine (US)
- Institute for Advanced Study (DE)
- University of Baltimore (US)
- University of Cincinnati (US)
- Chongqing Jiaotong University (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2025-11-06
- DOI
- https://doi.org/10.3390/su17219911
- Citations
- 1
- Primary Topic
- Transportation and Mobility Innovations
- Type
- article
- Field-Weighted Citation Impact
- 0.68