BusBeat: Early Event Detection with Real-Time Bus GPS Trajectories
BusBeat: Early Event Detection with Real-Time Bus GPS Trajectories
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DOI：
10.1109/tbdata.2018.2872532
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发表时间：
2018-09
影响因子：
7.2
通讯作者：
Shunsuke Aoki;K. Sezaki;Nicholas Jing Yuan;Xing Xie
中科院分区：
文献类型：
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作者：
Shunsuke Aoki;K. Sezaki;Nicholas Jing Yuan;Xing Xie
吸引众多参与者的大型活动可能会对城市的生产力、流动性、舒适度和安全性产生强烈的负面影响。近几年来，由于交通拥堵导致的严重交通事故时有发生，特别是在体育赛事、宗教仪式、节日等期间。为了缓解这些严重事故，预测大型事件的发生是非常重要的。当我们提前知道事件发生时，一些对事件不感兴趣的人可能会改变他们的计划和/或可能绕道而行以避免卷入严重的拥堵。在本文中，我们提出了一种名为BusBeat的早期事件检测技术。BusBeat使用从周期性车辆收集的GPS轨迹数据，周期性车辆是按照预定路线和预定出发时间定期行驶的车辆，例如公共汽车、班车、垃圾车或市政巡逻车。BusBeat利用周期性车辆的特征来插值缺失的GPS数据。此外，BusBeat使用基于网络的分析和时间相关的拥塞网络（TCN）来检测地理空间事件。BusBeat通过使用周期性车辆的连续轨迹提供实时交通流量和速度，在不侵犯隐私的情况下实现了早期事件检测。由于在活动开始之前，前往活动场所的交通很慢，因此BusBeat可以在参与者聚集之前检测地理空间事件。我们使用在北京5个月收集的7000多辆公交车的数据来评估我们的BusBeat，并与从社交网络服务收集的签到数据进行比较。
Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety in a city. Within the few years, serious accidents led by congestion have occurred, especially during sports events, religious ceremonies, festivals, and so on. To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat. BusBeat uses GPS trajectory data collected from periodic-cars that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. BusBeat interpolates the missing GPS data by using the features of the periodic-cars. In addition, BusBeat uses the network-based analysis with a Time-dependent Congestion Network (TCN) in order to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of the periodic-cars that provide the real-time traffic flow and speed. Since traffic towards an event venue would be slow before the event starts, BusBeat detects the geo-spatial events before the attendees gather. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.