Calibrating Real-World City Traffic Simulation Model Using Vehicle Speed Data

Abstract

Large-scale traffic simulations are necessary for the planning, design, and operation of city-scale transportation systems. These simulations enable novel and complex transportation technology and services such as optimization of traffic control systems, supporting on-demand transit, and redesigning regional transit systems for better energy efficiency and emissions. For a city-wide simulation model, big data from multiple sources such as Open Street Map (OSM), traffic surveys, geo-location traces, vehicular traffic data, and transit details are integrated to create a unique and accurate representation. However, in order to accurately identify the model structure and have reliable simulation results, these traffic simulation models must be thoroughly calibrated and validated against real-world data. This paper presents a novel calibration approach for a city-scale traffic simulation model based on limited real-world speed data. The simulation model runs a microscopic and mesoscopic realistic traffic simulation from Chattanooga, TN (US) for a 24-hour period and includes various transport modes such as transit buses, passenger cars, and trucks. The experiment results presented demonstrate the effectiveness of our approach for calibrating large-scale traffic networks using only real-world speed data. This paper presents our proposed calibration approach that utilizes 2160 real-world speed data points, performs sensitivity analysis of the simulation model to input parameters, and genetic algorithm for optimizing the model for calibration.