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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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    2. 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.
    3. Hong Yi & Lu Wang & Qiao Li & Xiang Li, 2022. "Investigate Jobs–Housing Spatial Relationship with Individual-Based Mobility Big Data of Public Housing Tenants: A Case Study in Chongqing, China," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    4. 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.
    5. 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.
    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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