Investigating Commuting Time Patterns of Residents Living in Affordable Housing: A Case Study in Nanjing, China

  • Jingxian Wu School of Transportation, Southeast University, Nanjing 210096, China
  • Min Yang
  • Soora Rasouli Urban Planning Group, Eindhoven University of Technology
  • Long Cheng Geography Department, Ghent University
Keywords: affordable housing, travel behaviour, trip time duration, decision tree classifier, built environment

Abstract

The phenomenon of affordable housing emerges in Chinese cities to meet low-income residents’ living needs in the city. Because affordable housing projects tend to be located far away from the city centre, their residents tend to face long commuting times to go to work. Although several studies have analysed commuting travel times, none have considered the commuting pattern of residents living in these affordable housing projects. This study employs a decision tree classifier to examine the commuting time patterns of affordable housing residents, fusing the data from the 2010 Nanjing Household Travel Survey and supplementary data collected through Google maps. Results show that attributes of the built environment and distance to work are the factors mostly influencing commuting time patterns of affordable housing residents in Nanjing. The availability of a subway service, job type, household car ownership, job location, travel mode choice, and departure time have logical but varying effects on commuting trip duration. These results provide a better understanding of these residents’ commuting patterns and provide urban planners insights about the effects of their affordable housing policies on travel behaviour.

Author Biographies

Min Yang

Professor

Soora Rasouli, Urban Planning Group, Eindhoven University of Technology

Associate Professor

Long Cheng, Geography Department, Ghent University

PhD

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Published
2019-08-10
How to Cite
1.
Wu J, Yang M, Rasouli S, Cheng L. Investigating Commuting Time Patterns of Residents Living in Affordable Housing: A Case Study in Nanjing, China. PROMET [Internet]. 2019Aug.10 [cited 2019Aug.25];31(4):423-3. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3012