付诗航, 刘耀林, 方莹, 杨孝军, 刘艳芳, 彭明军. 基于SCD的公共交通换乘时空模式——以武汉市为例[J]. 武汉大学学报 ( 信息科学版), 2020, 45(7): 1089-1098. DOI: 10.13203/j.whugis20190158
引用本文: 付诗航, 刘耀林, 方莹, 杨孝军, 刘艳芳, 彭明军. 基于SCD的公共交通换乘时空模式——以武汉市为例[J]. 武汉大学学报 ( 信息科学版), 2020, 45(7): 1089-1098. DOI: 10.13203/j.whugis20190158
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

基于SCD的公共交通换乘时空模式——以武汉市为例

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

  • 摘要: 换乘问题直接影响数十万乘客出行方便,已成为影响城市公共交通系统运营的一个重要因素。基于武汉市2015年3月份完整一周的公共交通智能卡数据(smart card data,SCD),识别公共交通换乘行为,研究乘客换乘时空特征,分析轨道交通发展现状,总结换乘出行模式。研究结果如下:①换乘行为在工作日和休息日均呈现早高峰单高峰分布,大概率存在加班行为;②换乘行为地理特征的3个影响因素为城市地理格局、轨道交通建设、站点辐射范围;③根据武汉市的圈层结构和换乘出行方向,将换乘行为总结为4种模式。基于交通大数据识别乘客换乘行为及其时空分布特征并归纳换乘模式,可为城市规划、城市空间合理利用提供科学依据和决策支持。

     

    Abstract: 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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