Graph Neural Network-Based Approach for Real-Time Traffic Congestion Prediction in Smart Cities

I. INTRODUCTION

Traffic congestion prediction models that treat intersections independently fail to capture the propagation of congestion along connected corridors. Graph-based learning offers a natural way to encode road network topology alongside temporal traffic patterns.

II. METHODOLOGY

Loop-detector readings from 340 signalised intersections over six months were assembled into a graph where nodes represent intersections and edges represent road segments weighted by distance and historical correlation. A two-layer graph attention network extracted spatial features at each timestep, which were passed to a GRU to model temporal dependencies, trained with a rolling-window scheme.

III. RESULTS AND EVALUATION

The model achieved MAPE of 9.8 percent, 11.4 percent and 13.9 percent at 15, 30 and 60-minute horizons respectively, consistently outperforming an LSTM-only baseline and a historical-average baseline across all horizons and across peak and off-peak periods.

IV. CONCLUSION

Encoding road network topology through graph attention meaningfully improves short-horizon congestion forecasts over sequence-only models. Future work will integrate weather and event data as additional graph features.

V. REFERENCES