地理科学进展

PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (6): 791-806.doi: 10.18306/dlkxjz.2019.06.001

• Articles •     Next Articles

Relationship between urban rail transit commuting and jobs-housing balance: An empirical analysis from Beijing based on big data methods

Lifan SHEN1(), Chun ZHANG2,*(), He LI3, Ye WANG4, Zijia WANG5   

  1. 1. School of Urban Design, Wuhan University, Wuhan 430072, China
    2. School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China
    3. International Finance Institute, Bank of China, Beijing 100818, China
    4. Guangzhou Planning & Design Survey Research Institute, Guangzhou 510030, China;
    5. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2018-07-10 Revised:2018-12-10 Online:2019-06-28 Published:2019-06-27
  • Contact: Chun ZHANG E-mail:495062785@qq.com;zhangc@bjtu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No. 51678029 and 51778039;Specific Research Project of China Urban Rail Transit Association, No. A17M00080.

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Abstract:

The development of urban rail transit (URT) network improves the commuting efficiency of residents while it has a certain impact on their jobs-housing balance. This study took 206 URT stations in Beijing as an example and classified them according to their jobs-housing functions based on the Gaussian mixture model (GMM) and smart card data. The dynamic population distribution characteristics around URT station were explored and jobs-housing ratio was calculated by "Yichuxing" position data. The study found that: 1) The jobs-housing balance in the central city is obviously better than that outside of the central city. 2) At the ends of the URT network, the jobs-housing balance is worse while only a few stations with concentrated distribution of top service industries have formed regional employment centers. 3) There still exists a certain degree of jobs-housing mismatch in the areas around some suburban stations where employment and residential functions are relatively equal. Station outflow-inflow and jobs-housing balances were calculated by the station egrass-ingrass ratio and the jobs-housing ratio, and the correlation between URT commuting behavior and jobs-housing balance was analyzed by generalized autoregressive conditional heteroskedasticity (GARCH) model. The results of this study indicate that: 1) There is a very strong positive relationship between URT station egrass-ingrass balance and jobs-housing balance. The closer the numbers of URT station outflow and inflow population, the better the jobs-housing balance around the URT station is. 2) There is a strong positive relationship between employment opportunity and jobs-housing balance around a URT station; and there is a strong negative relationship between residential function and jobs-housing balance around a URT station. This suggests that dense settlement will not generate the same quantity of jobs while well-developed employment hubs can attract a certain number of residents to live nearby. 3) There is a positive correlation between locational conditions of URT stations and jobs-housing balance. 4) The GMM can effectively cluster URT stations with complex and unclear attributes. 5) With its advantages of real-time data capturing, high precision, wide coverage, and great accessibility, "Yichuxing" position data can effectively compensate for the limitations of other methods on collecting and analyzing spatial-temporal characteristics of real-time population distribution at the microscopic scale.

Key words: urban rail transit, commuting behaviour, jobs-housing balance, big data, Gaussian mixture model, generalized autoregressive conditional heteroskedasticity (GARCH) model, Beijing