Transportation Engineering Lab

+82-2-880-7372 35-422

Transportation engineering is a study that explores the technology and engineering principles necessary for planning, designing, operating, and managing various transportation facilities so that people and logistics can move quickly, safely, and efficiently. Seoul National University's Transportation Engineering Lab aims to research the future transportation system that integrates and converges various fields. Our lab has been researching fields such as intelligent transportation systems, traffic management, network analysis, system design, and safety, focused on urban road networks and intercity highways. Recently, we are investigating connected and automated driving, smart city, smart signal, and MaaS (Mobility as a Service), which are global issues, using Operation Research, Econometrics, Machine (Deep) Learning, Discrete Choice Theory, and simulations as research tools. Courses cover various fields such as road engineering, sustainable transportation system, traffic flow theory, traffic system analysis, and traffic management.

This project aims to introduce citizens-oriented solutions for major urban problems, taking a step towards the future smart city. Along with a shared parking service as a solution for the urban parking problem, various transportation, safety, and environmental services are provided. Village enterprises are established around the city to enhance the sustainability of the smart city, creating jobs, and leading citizens to participate as prosumers. The implemented services are managed and provided on a single platform and application, increasing accessibility for the citizens. The goal of this project is to consider both characteristics and the needs of vulnerable transportation users in a new transportation system, MaaS (Mobility as a Service). MaaS is a new platform for utilizing the already existing transport network by bringing new digital interfaces to optimize the whole transport network with all its modes. We study optimizing the complex transit network, which depends on the needs of vulnerable transportation users. This study develops a novel method to monitor traffic flow using conventional detector systems on freeway and apply the method into traffic management strategies. Then, we conduct a simulation analysis for each strategy and reinforcement learning-based integrated strategy. This study aims to propose infrastructures, legal systems and traffic management strategies for supporting the autonomous driving. We suggest 3 steps and corresponding roadmaps for autonomous driving considering the outlook of autonomous driving introduction to freeways. And the results of this study enable to evaluate the current condition of freeway road for autonomous driving. The aim of this project is to develop road system that can verify and demonstrate cooperative automated driving by constructing test-bed and operating related technology. We develop longitudinal traffic management strategy for CAV (Cooperative/Connected Automated Vehicles) in this project.