Chen Guo
ABSTRACT
Users traveling by Shared Autonomous Vehicle (SAV) can make the most of their commute and reduce parking time and costs, while potential changes in residential location choices will be elaborated under SAV travel conditions. In order to explore the changes in the residential location under SAV travel conditions, this paper proposes a choice model combining the latent variables and performs elasticity analysis to better understand how residential locations change and what factors influence the residential location choice. Based on the questionnaire survey data conducted in China, this paper constructs a multinomial logit model of residential location selection with personal attributes, commuting information, shared travel attributes, SAV use willingness, and attitude latent variables (sharing mode, new technology, and autonomous vehicle) as explanatory variables. Groups with high education, long driving age, high parking costs at the workplace, having used shared cars, positive attitudes towards shared models and AV, and frequent usage of SAV will tend to relocate to downtown; groups that have suffered traffic accidents tend to relocate to the suburb. The results of the study suggest that first-tier cities in China will not grow in sprawl under SAV travel conditions, but that the attractiveness of suburban areas needs to be increased.
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Index Terms
- Residential Location Selection in First-Tier Cities in China under Shared Autonomous Vehicle Conditions
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