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Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model

机译:通过轨迹目的地预测预测收集事件:动态混合模型

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Identifying urban gathering events is an important problem due to challenges it brings to urban management. In our prior work, we proposed a hybrid model (H-VIGO-GIS) to predict future gathering events through trajectory destination prediction. Our approach consisted of two models: historical and recent and continuously predicted future gathering events. However, H-VIGO-GIS has limitations. (1) The recent model does not capture the newly-emerged abnormal patterns effectively, since it uses all recent trajectories, including normal ones. (2) The recent model is sparse due to limited number of trajectories it learns, i.e., it cannot produce predictions in many cases, forcing us to rely only on the historical model. (3) The accuracy of both recent and historical models varies by space and time. Therefore, combining them the same way at all times and places undermines the overall accuracy of the hybrid model. Addressing these issues, in this paper we propose a Dynamic Hybrid model called (DH-VIGO-TKDE) that addresses the above-mentioned issues. We perform comprehensive evaluations using two large real-world datasets and an event simulator. The experiments show the proposed model significantly improves the prediction accuracy and timeliness of forecasting gathering events, resulting in average precision of 0.91 and recall of 0.67 as opposed to 0.74 and 0.50 of H-VIGO-GIS.
机译:识别城市聚会事件是由于它带来城市管理的挑战,这是一个重要问题。在我们之前的工作中,我们提出了一个混合模型(H-Vigo-GIS)来通过轨迹目的地预测来预测未来的收集事件。我们的方法包括两个模型:历史和最近和不断预测的未来收集活动。但是,H-Vigo-GIS有局限性。 (1)最近的模型不会有效地捕获新出现的异常模式,因为它使用了所有最近的轨迹,包括普通轨迹。 (2)最近的模型是由于它学习的有限轨迹,即,它不能在许多情况下产生预测,强迫我们依靠历史模型依赖。 (3)最近和历史模型的准确性因空间和时间而异。因此,将它们与所有时间相同的方式,并且地点破坏了混合模型的整体准确性。在本文中解决这些问题,我们提出了一种称为(DH-Vigo-TKDE)的动态混合模型,用于解决上述问题。我们使用两个大型真实数据集和事件模拟器进行全面的评估。实验表明,提出的模型显着提高了预测收集事件的预测准确性和及时性,从而导致0.91的平均精度,并召回0.67,而不是0.74和0.50的H-Vigo-GIS。

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