Distinguishing commuters from non-commuters is important for transportation planning and traffic demand management. A framework to identify commuters using public transit is proposed based on a spatial-temporal clustering algorithm. The framework extracts commute trips by mining spatial-temporal travel patterns depending on whether the travelers' trip chains are complete. The commuting features in terms of travel frequency, time regularity, and spatial regularity are utilized to compare the difference in travel behavior between commuters and non-commuters. The framework is applied to trip data collected from an incentive program implemented by the Metropolitan Transport Commission of Korea. The data records spatial and temporal information on travelers' boarding and alighting points, as well as actual origins and destinations. One trip record constitutes a complete trip chain for travelers who joined the incentive program. The results show that commuters exhibit a higher travel frequency and more regular spatial-temporal travel patterns than non-commuters. It is found that not all commuters travel during morning and evening peaks; some commuters leave work after evening peak. The proposed identification framework based on the spatial-temporal clustering approach is capable of exploiting the inherent spatial-temporal travel patterns of travelers to achieve a reliable identification of commuters.

中文翻译：

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使用非机动模式激励计划和公共交通的出行链数据对通勤者进行时空识别


区分通勤者和非通勤者对于交通规划和交通需求管理非常重要。提出了一种基于时空聚类算法来识别使用公共交通的通勤者的框架。该框架根据旅行者的旅行链是否完整，通过挖掘时空旅行模式来提取通勤旅行。利用出行频率、时间规律性、空间规律性等通勤特征来比较通勤者与非通勤者出行行为的差异。该框架适用于从韩国都市交通委员会实施的激励计划中收集的出行数据。该数据记录了旅客上下车地点以及实际出发地和目的地的时空信息。一条旅行记录构成了参与激励计划的旅客的完整旅行链。结果表明，通勤者比非通勤者表现出更高的出行频率和更规律的时空出行模式。研究发现，并非所有通勤者都会在早晚高峰出行；一些通勤者在晚高峰后下班。所提出的基于时空聚类方法的识别框架能够利用旅行者固有的时空旅行模式来实现对通勤者的可靠识别。