A neural network model for road traffic flow estimation

Abstract

Real-time road traffic state information can be used for traffic flow monitoring, incident detection and other related traffic management activities. Road traffic state estimation can be done using either data driven or model based or hybrid approaches. The data driven approach is preferable for real-time flow prediction but to get traffic data for performance evaluation, hybrid approach is recommended. In this paper, a neural network model is employed to estimate real-time traffic flow on urban road network. To model the traffic flow, the microscopic model Simulation of Urban Mobility (SUMO) is used. The evaluation of the model using both simulation data and real-world data indicated that the developed estimation model could help to generate reliable traffic state information on urban roads.