| Title | Meteorology -Assisted Spatio-Temporal Graph Network for Uncivilized Urban Fvent Prediction |
|---|---|
| ID_Doc | 44466 |
| Authors | Luo, Y; Gu, ZH; Zhou, SY; Xiong, Y; Gao, XF |
| Title | Meteorology -Assisted Spatio-Temporal Graph Network for Uncivilized Urban Fvent Prediction |
| Year | 2023 |
| Published | |
| Abstract | Uncivilized urban events disrupt urban order and have a detrimental impact on daily life. Recognizing the significant implications of these events, urban managers strive to proactively prevent them by accurately predicting their future occurrence. However, existing methods overlook crucial contextual information within urban scenarios while mining spatio-temporal dependencies in single event series. Fortunately, we discovered a connection between meteorological conditions and uncivilized events. To leverage this relationship, we propose a novel approach named the Meteorology -Assisted Spatio-Temporal Graph Neural Network (MAST) which integrates meteorological information into the spatio-temporal dependency modeling for predicting urban uncivilized events. Additionally, our approach captures latent regularities in human behavior by explicitly modeling individuals' psychological states based on meteorological information. We also adopt cross-view contrastive learning between urban regions to dynamically capture the informative components of meteorological information for precise prediction of urban uncivilized events. Experimental evaluations on a real -world dataset demonstrate the superiority of MAST over state-of-theart baselines in terms of predictive performance. |
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