Event-driven Rerouting Framework for Agent Behavior in Urban Traffic Simulation

Traffic simulation is an essential tool for analyzing and predicting real-world traffic scenarios. It leverages real-world data to provide insights into various situations, even when data collection is impractical or inefficient. A critical aspect of simulation realism lies in developing sophisticated agent behavior models. In this paper, we propose an event-based rerouting framework for the UNIQ-SALT traffic simulation platform, designed to improve the realism of agent behaviors. Unlike static plans where vehicles follow predefined routes, this framework enables agents to dynamically respond to changing traffic conditions, facilitating the reproduction of diverse scenarios. Also, the framework was calibrated using real-world data from Daejeon in South Korea, and tested under a flood control event scenario. The evaluation, conducted through R-Square analysis of traffic flow, demonstrated high similarity between simulated and real-world data, achieving an R-Square (R²) value near 0.9. These results indicate the framework's effectiveness in accurately replicating real-world traffic dynamics during specific events. This research underscores the value of event-based models in enhancing simulation realism and supporting data-driven decision-making.