3 citations · 3 across the 4 of their papers we have counts for
6 papers
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer
Eunseung Lee, Gyeongjun Kim, Keemin Sohn
Transportation planning has traditionally required substantial expertise in network modeling, traffic assignment, and optimization. As a result, strategic analyses such as network…
A Decision-Focused Neural Optimization Framework for Personalized Route Reproduction from Vehicle Trajectories
Gyeongjun Kim, Yeseul Kang, Keemin Sohn
This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that, once such latent costs are…
Scalable Variational Inference for Multinomial Probit Models under Large Choice Sets and Sample Sizes
Gyeongjun Kim, Yeseul Kang, Lucas Kock +2
The multinomial probit (MNP) model is widely used to analyze categorical outcomes due to its ability to capture flexible substitution patterns among alternatives. Conventional like…
Multi-regime analysis for computer vision-based traffic surveillance using a change-point detection algorithm
Seungyun Jeong, Keemin Sohn
As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find…
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses
Byeonghyeop Yu, Johyun Shin, Gyeongjun Kim +2
Cameras for traffic surveillance are usually pole-mounted and produce images that reflect a birds-eye view. Vehicles in such images, in general, assume an ellipse form. A bounding…
Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner
Gyeongjun Kim, Keemin Sohn
Reinforcement learning (RL) algorithms have been widely applied in traffic signal studies. There are, however, several problems in jointly controlling traffic lights for a large tr…