My research interests are broad and interdisciplinary, encompassing Urban Mobility Data Analytics, Spatiotemporal Data Modeling, Deep Learning & Artificial Intelligence, and Connected Automated Vehicles (CAV) & Cooperative-ITS. I am particularly driven by the desire to optimize urban mobility and contribute to the development of sustainable and efficient urban transportation system. My work involves utilizing data analytics to draw valuable insights from urban mobility data and applying cutting-edge AI technologies in the field of transportation.

Education

Ph.D., 2021, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology
M.S., 2017, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology
B.S., 2015, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology

Professional Background

Postdoctoral Researcher, Institute of Applied Sciences, Korea Advanced Institute of Science and Technology, 2021
Postdoctoral Researcher, McGill University, 2022-2023

Selected Publications

Choi, S., Lee, D., Kim, S., and Tak S. (2023) “Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on Deep Reinforcement Learning: Field Test in Sejong City.” Transportation Research Part C: Emerging Technologies, 148, 104049.

Choi, S., Saunier, N., Trepanier, M., and Sun, L. (2022) “Spatiotemporal Residual Regularization with Kronecker Product Structure for Traffic Forecasting.” Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems.

Jin, Z., Kim, J., Yeo, H., and Choi, S. (2022) “Transformer-based map-matching model with limited labeled data using transfer-learning approach.” Transportation Research Part C: Emerging Technologies, 140, 103668.

Tak, S., and Choi, S. (2022) “Safety Monitoring System of CAVs Considering the Trade-off Between Sampling Interval and Data Reliability.” Sensors, 22(10), 3611.

Choi, S., Kim, J., and Yeo, H. (2021) “TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning.” Transportation Research Part C: Emerging Technologies, 128, 103091.

Choi, S., Yeo, H., and Kim, J. (2018) “Network-wide Vehicle Trajectory Prediction in Urban Traffic Networks Using Deep Learning” Transportation Research Record 2672.45: 173-184.