PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (3): 397-406.doi: 10.18306/dlkxjz.2018.03.010

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Investigating spatiotemporal patterns of passenger flows in the Beijing metro system from smart card data

Jie HUANG1(), Jiaoe WANG1,2, Haitao JIN1,2,3, Fengjun JIN1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Beijing Transportation Information Center, Beijing 100161, China
  • Received:2017-07-05 Revised:2018-01-08 Online:2018-03-28 Published:2018-03-28
  • Supported by:
    National Natural Science Foundation of China, No.41701132;Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19040402


Urban railway systems can reduce environmental footprints by residents' commuting and alleviate traffic congestion in mega-cities. Investigating the characteristics of the spatiotemporal distribution of passenger flows is significant in the examination of traffic demand in public transportation systems. Moreover, the study can help decision makers in traffic demand management. Taking the metro system of Beijing as an example, this study calculated the travel time of over 4 million trips and their origin-destination (OD) matrix. In the investigation of the spatiotemporal patterns, we found that: (1) travel time distribution of all trips and trips during the morning and afternoon peaks well fit with Gamma distribution; (2) patterns of passenger flows between districts or ring roads are symmetric; and (3) spatial inequity has been captured from the evaluation of average transit trips per person per day.

Key words: urban railway system, big data, passenger flow distribution, spatiotemporal pattern, Beijing