Journal article
Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model
IEEE transactions on knowledge and data engineering, Vol.33(3), pp.991-1004
03/01/2021
DOI: 10.1109/TKDE.2019.2937082
Abstract
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.
Details
- Title: Subtitle
- Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model
- Creators
- Amin Vahedian Khezerlou - University of IowaXun Zhou - University of IowaLing Tong - University of IowaYanhua Li - Worcester Polytechnic InstituteJun Luo - Machine Intelligence Center, Lenovo Group Limited, Hong Kong
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on knowledge and data engineering, Vol.33(3), pp.991-1004
- DOI
- 10.1109/TKDE.2019.2937082
- ISSN
- 1041-4347
- eISSN
- 1558-2191
- Publisher
- IEEE
- Grant note
- DiDi Chuxing Inc. IIS-1566386; CNS-1657350; CMMI-1831140 / National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 03/01/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380501202771
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