Research on road traffic condition prediction of smart city based on spatio-temporal multi-source information fusion
Chao Zheng, Qiankun Tang, Yanan Li · 2026
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Description
Chao Zheng and colleagues propose a 2026 deep learning method for smart-city road traffic prediction that fuses multi-source spatio-temporal data and handles abnormal events. Their dynamic graph network with multi-head attention and multi-task learning processes traffic and accident data for normal and disrupted conditions.
On three real data sets, the model beat the best baseline on mean absolute error for one benchmark and cut root mean square error by 18.7 percent against another on England highways. Long-horizon forecasts at 60 minutes also stayed ahead. City managers gain a stronger tool for traffic guidance under both routine and incident conditions.
Catalog record
| CREATOR | Chao Zheng, Qiankun Tang, Yanan Li |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |