Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks

Vehicle speed prediction provides important information for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging, because an individual vehicle speed is affected by many factors, e.g., the traffic condition, vehicle type, and driver's behavior, in either deterministic or stochastic way. This paper proposes a novel data-driven vehicle speed prediction method in the context of vehicular networks, in which the real-time traffic information is accessible and utilized for vehicle speed prediction. It first predicts the average traffic speeds of road segments by using neural network models based on historical traffic data. Hidden Markov models (HMMs) are then utilized to present the statistical relationship between individual vehicle speeds and the traffic speed. Prediction for individual vehicle speeds is realized by applying the forward-backward algorithm on HMMs. To evaluate the prediction performance, simulations are set up in the SUMO microscopic traffic simulator with the application of a real Luxembourg motorway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.