Projects

This research project aims to develop an AI-based origin–destination (OD) prediction framework to support future road policy planning and mobility service innovation. As emerging trends such as autonomous vehicles, electric vehicles, demographic changes, flexible work patterns, and regional mobility disparities reshape travel behavior, conventional demand forecasting methods face limitations in capturing uncertainty and dynamic behavioral changes. To address these challenges, the project proposes a scenario-based and data-driven forecasting framework that integrates heterogeneous mobility data, including telecommunications data, household travel surveys, and customized survey data. By applying advanced AI and data fusion techniques, the framework will estimate future OD demand, analyze changes in mode choice and travel behavior, and evaluate the impacts of emerging mobility services and infrastructure. The research will provide quantitative evidence for future highway policies and services, including autonomous public transport corridors, EV charging, transfer hubs, and demand-responsive mobility systems. Ultimately, the project seeks to enhance the adaptability, efficiency, and sustainability of Korea’s future highway network by supporting evidence-based decision-making under uncertain future mobility scenarios.

This project aims to validate the inter-operator fare settlement program and related research outcomes for the Seoul Metropolitan Transit System to improve the reliability and transparency of fare allocation among transit operators. The study involves comprehensive verification of network data, smart card data cleaning procedures, route search algorithms, and fare allocation calculations using real operational data and independent validation codes. Web crawling and algorithmic comparisons are conducted to assess the accuracy and rationality of route generation and settlement results, while inconsistencies and exceptional cases are identified and corrected through iterative feedback with development teams. The project seeks to confirm the reliability of the settlement framework and provide recommendations for enhancing route inference, exception handling, and future implementation of a daily settlement system.