Search Results for author:
Found 5 papers, 2 papers with code
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism.
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction.
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation
To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN).
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Additionally, there is a severe lack of fair comparison among different methods on the same datasets.
Ranked #2 on
Traffic Prediction
on NE-BJ
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks
Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range.
