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Exploring the impact of residential relocation on modal shift in commute trips: Evidence from a quasi-longitudinal analysis

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  • Yang, Min
  • Wu, Jingxian
  • Rasouli, Soora
  • Cirillo, Cinzia
  • Li, Dawei

Abstract

A growing number of studies have been devoted to the effects of residential relocation on travel behavior. However, most of these studies only focus on the direct effects of personal and system characteristics; while, residential relocation may trigger several interrelated changes in activity-travel behavior and mobility resources. This paper studies the mode choice of commuters who used active transport before relocating. Results from a Bayesian network (BN) analysis, trained on retrospective data collected in Nanjing, China, are presented. The constructed BN identifies significant statistical associations between modal shift and selected explanatory variables, which include movers’ socio-demographic characteristics, relocation-related attributes, and changes in built environment. Specifically, car ownership, income, additional car purchase, specific housing type and size, relocation type, change in commute distance, convenience of subway/bus for commuting, and distance to subway station are found to be important factors when deciding to switch from private car to public transit.

Suggested Citation

  • Yang, Min & Wu, Jingxian & Rasouli, Soora & Cirillo, Cinzia & Li, Dawei, 2017. "Exploring the impact of residential relocation on modal shift in commute trips: Evidence from a quasi-longitudinal analysis," Transport Policy, Elsevier, vol. 59(C), pages 142-152.
    • Handle: RePEc:eee:trapol:v:59:y:2017:i:c:p:142-152
    DOI: 10.1016/j.tranpol.2017.07.005
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    2. Xue, Fei & Yao, Enjian, 2022. "Impact analysis of residential relocation on ownership, usage, and carbon-dioxide emissions of private cars," Energy, Elsevier, vol. 252(C).
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    4. Chaoying Yin & Xiaoquan Wang & Chunfu Shao & Jianxiao Ma, 2022. "Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    5. Rahman, Mohammad Lutfur & Baker, Douglas, 2018. "Modelling induced mode switch behaviour in Bangladesh: A multinomial logistic regression approach," Transport Policy, Elsevier, vol. 71(C), pages 81-91.
    6. Sheng, Lu & Wu, Xiao & He, Yan, 2023. "Impact of residential relocation on activity-travel behaviors between household couples: A case study of Kunming, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    7. Guo, Jia & Feng, Tao & Timmermans, Harry J.P., 2019. "Time-varying dependencies among mobility decisions and key life course events: An application of dynamic Bayesian decision networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 82-92.
    8. Enhui Chen & Zhirui Ye & Hui Bi, 2019. "Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    9. Doddamani, Chetan & Manoj, M. & Maurya, Yashasvi, 2021. "Geographical scale of residential relocation and its impacts on vehicle ownership and travel behavior," Journal of Transport Geography, Elsevier, vol. 94(C).
    10. Yang, Zaili & Yang, Zhisen & Smith, John & Robert, Bostock Adam Peter, 2021. "Risk analysis of bicycle accidents: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
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    12. Morgan, Njogu, 2020. "The stickiness of cycling: Residential relocation and changes in utility cycling in Johannesburg," Journal of Transport Geography, Elsevier, vol. 85(C).
    13. Xue, Fei & Yao, Enjian & Jin, Fanglei, 2020. "Exploring residential relocation behavior for families with workers and students; a study from Beijing, China," Journal of Transport Geography, Elsevier, vol. 89(C).

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