Papers › Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Oluwasanmi A, Aftab MU, Qin Z, Sarfraz MS, Yu Y, Rauf HT
Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Traffic Prediction | SZ-Taxi | MHAST-GCN | MAE @ 15min | 2.6624 | #3 of 5 | Archive leaderboard | report |
| Traffic Prediction | SZ-Taxi | MHAST-GCN | MAE @ 30min | 2.7101 | #3 of 5 | Archive leaderboard | report |
| Traffic Prediction | SZ-Taxi | MHAST-GCN | MAE @ 45min | 2.7221 | #3 of 5 | Archive leaderboard | report |
| Traffic Prediction | SZ-Taxi | MHAST-GCN | MAE @ 60min | 2.7361 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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