Urban Population Migration Pattern Mining Based on Taxi Trajectories
Urban Population Migration Pattern Mining Based on Taxi Trajectories
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作者：
B. Zhu
了解城市人口迁移规律，对于城市的运营管理，包括交通预测、防疫、商业资源配置、应急响应以及未来的城市规划等都具有重要的指导意义。配备GPS的大型出租车车队包括城市地区无处不在的移动的探测器，它们的轨迹揭示了城市中的有趣现象。研究了基于出租车轨迹的城市人口迁移模式挖掘，包括热点区域识别和主要OD对提取。我们将展示如何应用最先进的点聚类算法来解决这两个任务。在2009年5月由8,000辆出租车生成的北京出租车轨迹数据集上对算法的性能进行了评估。结果显示有趣的见解的时间分辨的结果，这是一致的语义解释。
Understanding urban population migration patterns is very helpful for urban operation and management, including the traffic forecasting, epidemic prevention, commercial resource allocation, emergency response and future urban planning. The large taxi fleet equipped with GPS comprises ubiquitous mobile probes in urban areas, and their trajectories reveal interesting phenomena in the city. We investigate the urban population migration pattern mining based on taxi trajectories, including 1) the hotzone identification and 2) the principal OriginDestination traffic flow (OD pair) extraction. We show how to apply state-of-art point clustering algorithms to address these two tasks. The performance of the algorithms are evaluated on a Beijing taxi trajectory dataset generated by 8,000 taxicabs in May 2009. The results show interesting insights of the time-resolved results, which is consistent with semantic interpretations.