An Early Event Detection Technique with Bus GPS Data

Shunsuke Aoki, K. Sezaki, Nicholas Jing Yuan, Xing Xie
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引用次数: 7

Abstract

The analysis and study of the relationship between a geo-spatial event and human mobility in an urban area is very significant for improving productivity, mobility, and safety. In particular, in order to alleviate serious road congestions, traffic jams, and stampedes, it is essential to predict and be informed about the occurrence of an event as soon as possible. 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 context, this paper presents an early event detection technique using GPS trajectories collected from periodic-cars, which 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. Using these trajectories, which provide the real-time and continuous traffic flow and speed, our technique detects large-scale events in advance, without incurring any privacy invasion. The behavior of periodic-cars shows a certain sign of a large-scale event before attendees gather around a venue because traffic can be slowed around the venue before the event occurrence. We evaluated our method using over 7,000-bus data from January to May in 2015 in Beijing, which we compared with the check-in data collected from a social network service.
基于总线GPS数据的早期事件检测技术
分析和研究城市地理空间事件与人类流动性之间的关系,对于提高生产力、流动性和安全性具有重要意义。特别是,为了缓解严重的道路拥堵、交通堵塞和踩踏事件,尽早预测和了解事件的发生是至关重要的。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。在此背景下,本文提出了一种使用从周期性车辆收集的GPS轨迹的早期事件检测技术,周期性车辆是在预先安排的路线上以预先确定的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。利用这些轨迹,提供实时和连续的交通流量和速度,我们的技术可以提前检测大规模事件,而不会造成任何隐私侵犯。在与会者聚集在会场周围之前,周期性汽车的行为显示出大型活动的某种迹象,因为在活动发生之前,会场周围的交通可以减慢。我们使用2015年1月至5月在北京超过7000辆公交车的数据来评估我们的方法,我们将这些数据与从社交网络服务收集的签到数据进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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