Traffic Forecasting using Vehicle-to-Vehicle Communication
This addresses real-time traffic prediction for vehicles, but it is incremental as it builds on existing models with deep learning enhancements.
The paper tackles traffic forecasting by integrating first principle models with deep learning using vehicle-to-vehicle communication data, achieving improved accuracy in predicting individual vehicle velocities up to a minute into the future compared to baseline methods.
We take the first step in using vehicle-to-vehicle (V2V) communication to provide real-time on-board traffic predictions. In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning. Specifically, we train recurrent neural networks to improve the predictions given by first principle models. Our approach is able to predict the velocity of individual vehicles up to a minute into the future with improved accuracy over first principle-based baselines. We conduct a comprehensive study to evaluate different methods of integrating first principle models with deep learning techniques. The source code for our models is available at https://github.com/Rose-STL-Lab/V2V-traffic-forecast .