ST Forecasting

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and …

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction

As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predict- ing crime occurrences is of great importance for public safety and urban sustainability. However, existing methods do …

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations …

Predicting citywide crowd flows using deep spatio-temporal residual networks

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, …

DNN-Based Prediction Model for Spatial-Temporal Data

Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. …