Flophet: A Novel Prophet-Based Model for Traffic Flow Prediction in Vehicular Ad Hoc Networks

The rapid economic growth along with the population concentration in urban areas have led to caused urban traffic problem. It is one of the most serious problems in big cities that people have to deal in daily life. In recent years, researchers show wide interest in overcoming traffic issues where new models and frameworks have been rapidly developed for efficient traffic management in suitable vehicular environments; the emergence of Vehicular Ad Hoc Networks (VANETs). Nevertheless, traffic flow prediction is a major challenge in VANETs that has taken much attention. Subsequently, performing accurate and real-time traffic flow prediction plays an important role in reducing traffic congestion, saving traveler time, improving traffic safety, detect accidents rapidly, and reduce infrastructure damage. In this paper, we propose an efficient traffic prediction model called prophet traffic flow predictor (Flophet) for vehicular ad hoc networks. In this model, two major enhancement on the traditional neural prophet model were done. First, we propose an efficient algorithm for predicting the traffic flow trend and, then, we implement a new future regressor component called network mobility. Through simulations in real VANET data, we show the relevance of Flophet compared to other existing models.