8 citations · 11 across the 5 of their papers we have counts for
4 papers
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Hongjun Wang, Jiyuan Chen, Tong Pan +7
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, the…
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Hongjun Wang, Jiyuan Chen, Zipei Fan +3
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide lev…
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
Yifan Jiang, Zezheng Feng, Hongjun Wang +2
In recent years, deep learning approaches have been proved good performance in traffic flow prediction, many complex models have been proposed to make traffic flow prediction more…
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
Renhe Jiang, Zekun Cai, Zhaonan Wang +5
Nowadays, massive urban human mobility data are being generated from mobile phones, car navigation systems, and traffic sensors. Predicting the density and flow of the crowd or tra…