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

  • Jingxian Wu Department of Transportation Engineering, University of Shanghai for Science and Technology
  • 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


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 Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Min Yang


Soora Rasouli, Urban Planning Group, Eindhoven University of Technology

Associate Professor

Long Cheng, Geography Department, Ghent University



Woetzel J. Meeting China’s affordable housing challenge. McKinsey; 2015. Available from:

Nanjing Land Bureau. Nanjing Real Estate Yearbook; 2017. Available from:

Zhou Y. Contradictions in China's affordable housing policy: Goals vs. structure. Habitat International. 2014;41: 8-16.

Guo D, Li J, Wang Z. Research on spatial distribution characteristics of indemnificatory housing in Nanjing and optimizing strategies. Modern Urban Research. 2011;3: 83-88.

Song W. Characteristics, social problems and solutions of the spatial distribution of government-subsidized houses in Chinese mega-cities. International Journal of Urban Sciences. 2011;15(2):107-119.

Zhao P. The impact of the built environment on individual workers’ commuting behavior in Beijing. International Journal of Sustainable Transportation. 2013;7(5): 389-415.

Wang D, Zhou M. The built environment and travel behavior in urban China: A literature review. Transportation Research Part D: Transport and Environment. 2017;52: 574-585.

Gan X, Zuo J, Wu P, Wang J, Chang R, Wen T. How affordable housing becomes more sustainable? A Stakeholder Study. Journal of Cleaner Production. 2017;162: 427-437.

Li X, Du J. Research on private cars’ influence on residential space based on residents’ travel behavior: Taking Dalian as an example. Geographical Research. 2007;5:020. Chinese.

Morris EA, Guerra E. Are we there yet? Trip duration and mood during travel. Transportation Research Part F: Traffic Psychology and Behaviour. 2015;33: 38-47.

Schwanen T, Dijst M, Dieleman FM. A microlevel analysis of residential context and travel time. Environment and Planning A. 2002;34(8): 1487-1507.

Wang D, Chai Y. The jobs–housing relationship and commuting in Beijing, China: The legacy of Danwei. Journal of Transport Geography. 2009;17(1): 30-38.

Susilo YO, Maat K. The influence of built environment to the trends in commuting journeys in the Netherlands. Transportation. 2007;34(5):589.

Cervero R. Jobs-housing balancing and regional mobility. Journal of the American Planning Association. 1989;55(2): 136-150.

Schwanen T, Dieleman FM, Dijst M. Car use in Netherlands daily urban systems: Does polycentrism result in lower commute times? Urban geography. 2003;24(5): 410-430.

Kwan MP, Kotsev A. Gender differences in commute time and accessibility in Sofia, Bulgaria: a study using 3D geovisualisation. The Geographical Journal. 2014;181(1): 83-96.

Gimenez-Nadal JI, Molina JA. Commuting time and household responsibilities: Evidence using propensity score matching. Journal of Regional Science. 2016;56(2): 332-359.

Fan Y. Household structure and gender differences in travel time: Spouse/partner presence, parenthood, and breadwinner status. Transportation. 2017;44(2): 271-291.

Vincent-Geslin S, Ravalet E. Determinants of extreme commuting. Evidence from Brussels, Geneva and Lyon. Journal of Transport Geography. 2016;54: 240-247.

Scheiner J, Holz-Rau C. Gendered travel mode choice: A focus on car deficient households. Journal of Transport Geography. 2012;24: 250-261.

He M, Zhao S. Determinants of long-duration commuting and long-duration commuters' perceptions and attitudes toward commuting time: Evidence from Kunming, China. IATSS Research. 2017;41(1): 22-29.

Sultana S. Job/housing imbalance and commuting time in the Atlanta metropolitan area: exploration of causes of longer commuting time. Urban Geography. 2002;23(8): 728-749.

Dargay JM, Van Ommeren J. The effect of income on commuting time using panel data. 45th Conference of the European Regional Science Association at the Vrije Universiteit Amsterdam; 2005.

Bento AM, Cropper ML, Mobarak AM, Vinha K. The effects of urban spatial structure on travel demand in the United States. Review of Economics and Statistics. 2005;87(3): 466-478.

Frank L, Bradley M, Kavage S, Chapman J, Lawton TK. Urban form, travel time, and cost relationships with tour complexity and mode choice. Transportation. 2008;35(1): 37-17.

Zhao P, Lu B, De Roo G. Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era. Journal of Transport Geography. 2011;19(1): 59-69.

Sun B, He Z, Zhang T, Wang R. Urban spatial structure and commute duration: An empirical study of China. International Journal of Sustainable Transportation. 2016;10(7): 638-644.

Zhao P, Lu B. Exploring job accessibility in the transformation context: An institutionalist approach and its application in Beijing. Journal of Transport Geography. 2010;18(3): 393-401.

Arentze T, Timmermans H. Parametric action decision trees: Incorporating continuous attribute variables into rule-based models of discrete choice. Transportation Research Part B: Methodological. 2007;41(7): 772-783.

Kashani AT, Mohaymany AS. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science. 2011;49(10): 1314-1320.

De Oña J, de Oña R, Calvo FJ. A classification tree approach to identify key factors of transit service quality. Expert Systems with Applications. 2012;39(12): 11164-11171.

Zhang J, Yu B, Chikaraishi M. Interdependences between household residential and car ownership behavior: A life history analysis. Journal of Transport Geography. 2014;34: 165-174.

Tang L, Xiong C, Zhang L. Decision tree method for modeling travel mode switching in a dynamic behavioural process. Transportation Planning and Technology. 2015;38(8): 833-850.

Kohavi R, John GH. Wrappers for feature subset selection. Artificial intelligence. 1997;97(1-2): 273-324.

Quinlan JR. Induction of decision trees. Machine Learning. 1986;1(1): 81-106.

Fawcett T. ROC graphs: Notes and practical considerations for researchers. Machine Learning. 2004;31(1): 1-38.

Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning, 25-29 June 2006, Pittsburgh, Pennsylvania, USA. New York: ACM; 2006. p. 233-240.

Kotsiantis S, Kanellopoulos D. Discretization techniques: A recent survey. GESTS International Transactions on Computer Science and Engineering. 2006;32(1): 47-58.

Yang M, Wu J, Rasouli S, Cirillo C, Li D. Exploring the impact of residential relocation on modal shift in commute trips: Evidence from a quasi-longitudinal analysis. Journal of Transport Policy. 2017;59: 142-152.

Cervero R, Day J. Residential Relocation and Commuting Behavior in Shanghai, China: The Case for Transit Oriented Development. Institute of Transportation Studies, UC Berkeley. Research Reports, Working Papers, Proceedings qt0dk1s0q5, 2008.

Yang J. Transportation implications of land development in a transitional economy: Evidence from housing relocation in Beijing. Journal of the Transportation Research Board. 2006;1954: 7-14.

Wang D, Zhou M. The built environment and travel behavior in urban China: A literature review. Transportation Research Part D: Transport and Environment. 2017;52: 574-585.

How to Cite
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 2024Jun.12];31(4):423-3. Available from: