Vehicle Travel Time Estimation in Transportation Network Using Random Forest and Neural Network

Abstract

Several technologies in Intelligent Transportation Systems (ITS), such as automatic driving, electric vehicles, vehicular communications, are reshaping the way we travel and use the transportation system. Automatization of ITS mainly consists of traffic management, traffic light control, optimal route selection and so on. In these automatic applications, accurate estimation of vehicle travel time is essential to make efficient decisions. This study uses synthetic traffic data generated by Simulation of Urban MObility (SUMO) to evaluate machine learning models, like Random Forest and Neural Network, for travel time estimation. A variety of traffic-related features were collected, and three feature scenarios were tested. Results show that the Random Forest model outperforms both the neural network and the baseline method based on numerical estimation, highlighting the benefit of feature-rich approaches.