Intelligent speed profile prediction on urban traffic networks with machine learning

Abstract

Accurate prediction of traffic information such as flow, density, speed, and travel time is an important component for traffic control systems and optimizing vehicle operation. Prediction of an individual speed profile on an urban network is a challenging problem because traffic flow on urban routes is frequently interrupted and delayed by traffic lights, stop signs, and intersections. In this paper, we present an Intelligent Speed Profile Prediction on Urban Traffic Network (ISPP_UTN) that can predict a speed profile of a selected urban route with available traffic information at the trip starting time. ISPP_UTN consists of four speed prediction Neural Networks (NNs) that can predict speed in different traffic areas. ISPP_UTN takes inputs from three different categories of traffic information such as the historical individual driving data, geographical information, and traffic pattern data. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profiles are close to the real recorded speed profiles.