1. Speaker: Daejin Kim Assistant Professor (Inha University)
 

2. Date: December 5, 2024 (Thursday) 16:00 ~ 17:30
 

3. Venue: KAIST Munji Campus Lecture Hall L409
 

 
4. Abstract: The integration of AI-based systems into traffic and emissions modeling represents a significant advancement in understanding and mitigating urban environmental impacts. This study explores the application of Real Driving Emissions (RDE) data, alongside advanced simulation tools such as POLARIS, Autonomie, and MOVES-Matrix, to develop robust models for predicting traffic patterns and associated emissions. The RDE data provides a high-resolution, realworld basis for emissions, capturing variability in driving conditions that traditional lab-based tests may overlook. POLARIS offers a dynamic traffic simulation platform that, when combined with Autonomie’s detailed vehicle powertrain models, allows for precise estimation of vehicle energy consumption and emissions. MOVES-Matrix, with its comprehensive emissions inventories, complements these models by enabling large-scale emissions analysis across different scenarios. By leveraging AI, these tools can be integrated to predict the environmental impacts of various traffic management strategies and vehicle technologies, providing policymakers with actionable insights for reducing urban air pollution and optimizing traffic flow. The results underscore the potential of AI-enhanced modeling systems in developing more sustainable and efficient transportation systems