FU Shihang, LIU Yaolin, FANG Ying, YANG Xiaojun, LIU Yanfang, PENG Mingjun. Spatiotemporal Pattern of Public Transit Behavior Based on Smart Card Data—A Case Study of Wuhan City[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 1089-1098. DOI: 10.13203/j.whugis20190158
Citation: FU Shihang, LIU Yaolin, FANG Ying, YANG Xiaojun, LIU Yanfang, PENG Mingjun. Spatiotemporal Pattern of Public Transit Behavior Based on Smart Card Data—A Case Study of Wuhan City[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 1089-1098. DOI: 10.13203/j.whugis20190158

Spatiotemporal Pattern of Public Transit Behavior Based on Smart Card Data—A Case Study of Wuhan City

  • With the development of China's rail transit system, the transfer problem directly affects the convenience of hundreds of thousands of passengers day after day, and has become an important factor which affects the operation of public transportation systems. The problem of public transportation transfer should be"people-oriented" and analyzed from the time and space mode of passengers' transfer. Based on the smart card data (SCD) of Wuhan City in March 2015, this paper studies the time-space characteristics of passenger transfer, analyzes the current situation of rail transit development, and summarizes the transfor travel mode. The research results are as follows:① In the process of the development of rail transit in Wuhan, the imbalance of rail transit services has arisen and a single-center urban structure has emerged. ② The geographical characteristics of transfer behavior are related to the city's own geographical pattern, and three factors which influence the transfer behavior are proposed. ③ Using intelligent bus card data, it can effectively identify the social behavior of passengers (transfer behavior) and the time and space mode of passengers traveling. This paper analyzes the spatiotemporal patterns of travel behaviors such as transfer behavior and urban planning and urban space utilization from the perspective of "people-oriented" based on the analysis of big data.
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