Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction

arXiv:2404.15034v1 Announce Type: new
Abstract: Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diverse impacts of the various traffic observations (e.g. vehicle speed and road occupancy) on the traffic flow prediction, and different traffic observations can be considered as different …

abstract applications arxiv challenges correlations cs.ai cs.lg data dynamic flow forecasting intelligent intelligent transportation mobility network patterns people prediction public public safety safety spatial systems temporal traffic transportation type urban view wise