Commuter flow is an important part of metro passenger flow. The aim of this study is to develop an efficient and effective method to identify the spatiotemporal commuting patterns of Metro Line 2 in Hangzhou. Using one-week transit smart card data and a questionnaire survey of Metro Line 2, the authors distinguished the spatiotemporal regularity of individual commuters, including first travel time, last travel time, and the number of travelling days on weekdays. This data could be used to identify transit commuters by leveraging ensemble learning approaches. The random forest algorithm was adopted as a low-cost, high-efficiency analysis method, and the classification model was established with the information of travel time, days of travelling, and the unique tag information in the questionnaire survey data. Then, numerical tests were carried out to show that the Precision and Recall rates of the proposed model could reach as high as 0.96 and 0.92, respectively. Finally, the validated random forest model was applied to identify metro commuters from the smartcard data. The results show that less than one-third of passengers are commuter traffic and are mainly concentrated during peak hours. These extracted personal-level commute models can be used as valuable information for the design and optimisation of public transportation networks.

中文翻译：

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基于智能卡数据的随机森林识别通勤者

通勤流量是地铁客流的重要组成部分。这项研究的目的是开发一种有效的方法来识别杭州地铁2号线的时空通勤模式。通过使用一周的公交智能卡数据和地铁2号线的问卷调查，作者区分了各个通勤者的时空规律，包括第一次出行时间，最后一次出行时间以及平日的出行天数。该数据可通过集成学习方法用于识别通勤通勤者。采用随机森林算法作为一种低成本，高效的分析方法，并根据问卷调查数据中的出行时间，出行天数和唯一标签信息建立分类模型。然后，数值测试表明，所提模型的查准率和查全率分别可以达到0.96和0.92。最后，将经过验证的随机森林模型应用于从智能卡数据中识别地铁通勤者。结果表明，只有不到三分之一的乘客是通勤交通，并且主要集中在高峰时段。这些提取的个人级别的通勤模型可以用作设计和优化公共交通网络的有价值的信息。结果表明，只有不到三分之一的乘客是通勤交通，并且主要集中在高峰时段。这些提取的个人级别的通勤模型可以用作设计和优化公共交通网络的有价值的信息。结果表明，只有不到三分之一的乘客是通勤交通，并且主要集中在高峰时段。这些提取的个人级别的通勤模型可以用作设计和优化公共交通网络的有价值的信息。