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.

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 R2value.

A grid technology is one of key issues for healthcare services provided in collaborative environments. In this paper, a SLA-adaptive workflow integrated grid resource management system for supporting collaborative heart disease simulator application in Physio-Grid service is proposed. At first, we propose the system architecture and framework that integrates workflow function into policy quorum based resource management (PQRM) system, one of existing grid resource management systems, for collaborative healthcare services. In addition, we derived the cost-adaptive policy adjustment scheme to adjust a gap between conditions and actions of workflow management and resource management policies for the proposed system. Based on this adjustment scheme, an appropriate policy can be selected according to QoS constraints of collaborated healthcare applications given by SLA negotiated with users. Finally, we evaluate proposed system using a collaborative heart disease simulation service and show the proposed system outperforms a general grid management system throughout performance comparisons under different types of SLA.