Recurrent Traffic Demand Generation using Urban Traffic Simulation with Cell-based Behavior Model and Real Traffic Data

Abstract

The city traffic simulation is one of solutions to analyze and forecast the traffic state for urban road network. In order to simulate the real traffic situation well, the fine-tuning simulation inputs, including road network and traffic demands, closer to real measured data is important. In this paper, we propose the UNIQ-SALT, which is a cell-based traffic simulator, with the RTDG (Recurrent Traffic Demand Generation) model as an adjustment model for the simulation inputs. This RTDG model recurrently calibrates the simulation results with real traffic data in Daejeon and Sejong, South Korea until obtaining predefined target error rate between simulated and real values. Finally, we show the simulated result is under 10% error coverage, MAPE, on main spots of the simulation area and the correlation between the simulated data and the real data is reasonable as near 0.9 of the R2 value.