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

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.

Automated summary

Researchers proposed a Dynamic Hybrid model (DH-VIGO-TKDE) to predict urban gathering events, addressing the limitations of previous models. The experiments showed that the model significantly improved accuracy and timeliness in forecasting, with an average precision of 0.91 and recall of 0.67 compared to H-VIGO-GIS's 0.74 and 0.50.