Seongjin Choi

Emailchoisumnedu

Accepting PhD Students

PhD projects

My research interests are broad and interdisciplinary, encompassing Urban Mobility Data Analytics, Spatiotemporal Data Modeling, Deep Learning and Artificial Intelligence, and Connected Automated Vehicles (CAV) and Cooperative-ITS. I am particularly driven by the desire to optimize urban mobility and contribute to the development of a 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.

My research topics and interests are as follows:

Urban transportation and mobility data analytics
Spatiotemporal data modeling (forecasting, imputation)
Generative AI for transportation and mobility data
Modeling CAV, C-ITS using Reinforcement Learning
Other applications of machine learning and deep learning in the transportation domain

Research activity per year

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

Education/Academic qualification

PhD, Korea Advanced Institute of Science and Technology (KAIST)

Mar 1 2017 → Aug 20 2021

MS, Korea Advanced Institute of Science and Technology (KAIST)

Sep 1 2015 → Feb 15 2017

BS, Korea Advanced Institute of Science and Technology (KAIST)

Feb 1 2011 → Aug 20 2015

External Positions

Postdoctoral Researcher, McGill University

Jan 1 2022 → Dec 31 2023

Postdoctoral Researcher, Korea Advanced Institute of Science and Technology (KAIST)

Sep 1 2021 → Nov 30 2021

Research Interest Keywords

Transportation
AI for Transportation
Connected and Autonomous Vehicles
Spatiotemporal Data Modeling
Traffic Simulation
Traffic Operations

Fingerprint

The Fingerprint is created by mining the titles and abstracts of the person's research outputs and projects/funding awards to create an index of weighted terms from discipline-specific thesauri.

1 Similar Profiles

9 Article
8 Conference contribution
3 Preprint
2 Conference article

Optimal planning of parking infrastructure and fleet size for Shared Autonomous Vehicles
Choi, S. & Lee, J., Aug 2023, In: Transportation Research Part E: Logistics and Transportation Review.
Research output: Contribution to journal › Article › peer-review

3 Scopus citations
Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City
Choi, S., Lee, D., Kim, S. & Tak, S., Mar 2023, In: Transportation Research Part C: Emerging Technologies.
Research output: Contribution to journal › Article › peer-review

18 Scopus citations
TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning
Choi, S., Kim, J. & Yeo, H., 2021, In: Transportation Research Part C: Emerging Technologies.
Research output: Contribution to journal › Article › peer-review

91 Scopus citations
Transformer-based map matching model with limited ground-truth data using transfer-learning approach
Jin, Z., Kim, J., Yeo, H. & Choi, S., 2021, In: Transportation Research Part C: Emerging Technologies.
Research output: Contribution to journal › Article › peer-review
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Choi, S., Saunier, N., Zheng, V. Z., Trepanier, M. & Sun, L., Dec 10 2022.
Research output: Working paper › Preprint

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Seongjin Choi

Personal profile

Expertise related to UN Sustainable Development Goals

Education/Academic qualification

External Positions

Research Interest Keywords

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Optimal planning of parking infrastructure and fleet size for Shared Autonomous Vehicles

Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

Transformer-based map matching model with limited ground-truth data using transfer-learning approach

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

Seongjin Choi

Personal profile

Expertise related to UN Sustainable Development Goals

Education/Academic qualification

External Positions

Research Interest Keywords

Fingerprint

Collaborations and top research areas from the last five years

Research output

Optimal planning of parking infrastructure and fleet size for Shared Autonomous Vehicles

Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

Transformer-based map matching model with limited ground-truth data using transfer-learning approach

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting