53 citations · 153 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
Zhiwen Zhang, Hongjun Wang, Jiyuan Chen +3
Estimating the travel time of a path is an essential topic for intelligent transportation systems. It serves as the foundation for real-world applications, such as traffic monitori…
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
Zipei Fan, Xiaojie Yang, Wei Yuan +4
Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that m…
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
Zhiwen Zhang, Hongjun Wang, Zipei Fan +3
Due to the rapid development of Internet of Things (IoT) technologies, many online web apps (e.g., Google Map and Uber) estimate the travel time of trajectory data collected by mob…
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…
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization
Mingxin Zhang, Zipei Fan, Ryosuke Shibasaki +1
In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile com…
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…