Traffic Forecasting using Vehicle-to-Vehicle Communication

TL;DR

This paper introduces a novel approach combining first principle models with deep learning, specifically recurrent neural networks, to enhance real-time vehicle velocity predictions using vehicle-to-vehicle communication data.

Contribution

It is the first to integrate first principle models with deep learning for V2V traffic prediction, achieving improved accuracy in velocity forecasting.

Findings

Predicts vehicle velocity up to one minute ahead with higher accuracy

Demonstrates effectiveness of combining physics-based models with neural networks

Provides open-source code for reproducibility

Abstract

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 .