Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data
Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data
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DOI：
10.1145/3356473.3365190
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Alex Lam;Matthew Schofield;S. Ho
中科院分区：
文献类型：
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作者：
Alex Lam;Matthew Schofield;S. Ho
社交媒体、交通传感器、GPS轨迹和基于位置的社交网络数据提供了多种时空信息源，有助于检测和分析时空事件。如今，自行车共享系统在世界各地的主要城市都很活跃，并收集了大量关于用户出行和站点状态的数据。通过分析共享单车系统汇总的数据，人们可以了解人群/通勤者的运动和行为。然而，还没有人仅仅使用共享单车数据进行通用事件检测。在本文中，我们提出了一种基于聚类的检测方法，利用公开的共享单车数据来识别偏离正常或常规日常生活的时空事件。特别是，我们将自行车站和自行车流数据作为演化图应用谱聚类，并监测共享单车网络（边缘/节点值）随时间的变化。我们提出的方法决定一个集群是预期的还是异常的。当集群处于异常状态时，表示在该时间实例上发生了异常事件。通过对费城和华盛顿6个月的数据进行初步分析，证明了该方法的可行性。特别是，我们的初步结果表明，当将共享单车数据用于一般事件检测时，可以显示本地（和不太突出的）事件（例如，城市地区的大学事件/活动）的一些特征。
Social media, traffic sensors, GPS trajectories, and location-based social network data provide diverse Spatio temporal information sources that help to detect and analysis Spatio temporal events. Nowadays, bike sharing systems are active all over the world in major cities, and collecting a large amount of data regarding trips taken by users and status of the stations. Through analysis of the data aggregated by bike sharing systems, one can gain an understanding of crowd/commuter movements and behaviors. However, no one has used only the bike sharing data for generic event detection. In this paper, we propose a clustering-based detection method to identify Spatio temporal events that deviate from normal or regular everyday life using publicly available bike sharing data. In particular, we apply spectral clustering on bike station and bike flow data as evolving graphs and monitor changes of the bike share network (edge/node values) over time. Our proposed method decides whether a cluster is expected or anomalous (unusual). When a cluster is anomalous, there is an unusual event occurring at that time instance. Preliminary results on 6-months of data from Philadelphia and Washington DC are used to show the feasibility of our proposed method. In particular, our preliminary results show that some signatures of local (and less prominent) events (e.g., university events/activities in an urban area) can show up when bike sharing data is utilized for generic event detection.