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Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China

Author

Listed:
  • Meina Zheng

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Feng Liu

    (School of Economics and Management, Southeast University, Nanjing 211189, China)

  • Xiucheng Guo

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Xinyue Lei

    (School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

The purpose of this research is to assess the distribution of commuting trips and the level of jobs-housing balance with Nanjing smart card data. A new approach is presented using the Lorenz curve and Gini coefficient based on the commuting time. This article also quantifies and visualizes Nanjing’s jobs-housing balance in each urban, suburban and exurban district. The core findings from this research are summarized as follows. First, the Gini coefficient of commuting time is 0.251 in urban areas, 0.258 for suburban areas and 0.267 for exurban areas. The gap of each non-urban district in commuting time is larger than urban districts. Second, the result of jobs-housing ratio (JHR) shows that jobs of Xuanwu district are far more than the working population of this district, whereas jobs and working population in other urban districts are relatively matched. The value of JHR is less than 0.8 in all suburban and exurban districts but Yuhuatai district, which suggests that jobs in these suburban districts (excluding Yuhuatai district) are in short supply compared with their working population. Third, the JHR within a particular district may be different according to the specific locations, especially those areas close to the boundary between two different kinds of districts.

Suggested Citation

  • Meina Zheng & Feng Liu & Xiucheng Guo & Xinyue Lei, 2019. "Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5346-:d:271390
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    as
    1. Paul D. Gottlieb & Barry Lentnek, 2001. "Spatial Mismatch is not Always a Central-city Problem: An Analysis of Commuting Behaviour in Cleveland, Ohio, and its Suburbs," Urban Studies, Urban Studies Journal Limited, vol. 38(7), pages 1161-1186, June.
    2. Jenelius, Erik, 2012. "The value of travel time variability with trip chains, flexible scheduling and correlated travel times," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 762-780.
    3. Harry J. Holzer & John M. Quigley & Steven Raphael, 2003. "Public transit and the spatial distribution of minority employment: Evidence from a natural experiment," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 22(3), pages 415-441.
    4. Valerie Preston & Sara McLafferty, 1999. "articles: Spatial mismatch research in the 1990s: progress and potential," Papers in Regional Science, Springer;Regional Science Association International, vol. 78(4), pages 387-402.
    5. Cervero, Robert & Day, Jennifer, 2008. "Suburbanization and transit-oriented development in China," Transport Policy, Elsevier, vol. 15(5), pages 315-323, September.
    6. Wachs, Martin & Kumagai, T. Gordon, 1973. "Physical accessibility as a social indicator," Socio-Economic Planning Sciences, Elsevier, vol. 7(5), pages 437-456, October.
    7. Donald S. Houston, 2005. "Methods to Test the Spatial Mismatch Hypothesis," Economic Geography, Taylor & Francis Journals, vol. 81(4), pages 407-434, October.
    8. Zhong-Ren Peng, 1997. "The Jobs-Housing Balance and Urban Commuting," Urban Studies, Urban Studies Journal Limited, vol. 34(8), pages 1215-1235, July.
    9. Mark W Horner & Jessica N Mefford, 2007. "Investigating Urban Spatial Mismatch Using Job–Housing Indicators to Model Home–Work Separation," Environment and Planning A, , vol. 39(6), pages 1420-1440, June.
    10. Frank Primerano & Michael Taylor & Ladda Pitaksringkarn & Peter Tisato, 2008. "Defining and understanding trip chaining behaviour," Transportation, Springer, vol. 35(1), pages 55-72, January.
    11. Anas, Alex, 2002. "Prejudice, exclusion, and compensating transfers: the economics of ethnic segregation," Journal of Urban Economics, Elsevier, vol. 52(3), pages 409-432, November.
    12. David Levinson, 1998. "Accessibility and the Journey to Work," Working Papers 199802, University of Minnesota: Nexus Research Group.
    13. Chen, Jie & Ni, Jianhua & Xi, Changbai & Li, Siqian & Wang, Jiechen, 2017. "Determining intra-urban spatial accessibility disparities in multimodal public transport networks," Journal of Transport Geography, Elsevier, vol. 65(C), pages 123-133.
    14. Zhang, Mengzhu & He, Shenjing & Zhao, Pengjun, 2018. "Revisiting inequalities in the commuting burden: Institutional constraints and job-housing relationships in Beijing," Journal of Transport Geography, Elsevier, vol. 71(C), pages 58-71.
    15. Cervero, Robert, 1989. "Jobs-Housing Balancing and Regional Mobility," University of California Transportation Center, Working Papers qt7mx3k73h, University of California Transportation Center.
    16. Seongman Jang & Youngsoo An & Changhyo Yi & Seungil Lee, 2017. "Assessing the spatial equity of Seoul’s public transportation using the Gini coefficient based on its accessibility," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(1), pages 91-107, January.
    17. Carleton, Phillip R. & Porter, J. David, 2018. "A comparative analysis of the challenges in measuring transit equity: definitions, interpretations, and limitations," Journal of Transport Geography, Elsevier, vol. 72(C), pages 64-75.
    18. Pengyu Zhu, 2016. "Residential segregation and employment outcomes of rural migrant workers in China," Urban Studies, Urban Studies Journal Limited, vol. 53(8), pages 1635-1656, June.
    19. Lingqian Hu, 2017. "Job accessibility and employment outcomes: which income groups benefit the most?," Transportation, Springer, vol. 44(6), pages 1421-1443, November.
    20. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
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    2. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    3. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
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    5. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    6. Haonan Zhang & Hu Zhao & Saisai Meng & Yanghua Zhang, 2022. "Research on the Jobs-Housing Balance of Residents in Peri-Urbanization Areas in China: A Case Study of Zoucheng County," Sustainability, MDPI, vol. 14(13), pages 1-24, June.

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