181 citations · 263 across the 13 of their papers we have counts for
13 papers
Beyond Averages: Evaluating LLMs on Human Survey Replication at the Distributional Level
Jeonghyeon Moon, Jiwon Kim, Yeheum Lah +2
LLMs are increasingly used to simulate human survey responses, but prior work has mainly evaluated replication using mean-level or aggregate agreement, offering limited insight int…
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth
Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu +2
Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployme…
MedROI: Codec-Agnostic Region of Interest-Centric Compression for Medical Images
Jiwon Kim, Ikbeom Jang
Medical imaging archives are growing rapidly in both size and resolution, making efficient compression increasingly important for storage and data transfer. Most existing codecs co…
Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark
Artur Grigorev, Khaled Saleh, Jiwon Kim +1
Traffic incidents remain a critical public safety concern worldwide, with Australia recording 1,300 road fatalities in 2024, which is the highest toll in 12 years. Similarly, the U…
evS2CP: Real-time Simultaneous Speed and Charging Planner for Connected Electric Vehicles
Minwoo Gwon, Jiwon Kim, Seungjun Yoo +1
This paper presents evS2CP, an optimization-based framework for simultaneous speed and charging planning designed for connected electric vehicles (EVs). With EVs emerging as compet…
A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
Seongjin Choi, Zhixiong Jin, Seung Woo Ham +2
Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generat…
Understanding everyday public transit travel habits: a measurement framework for the peakedness of departure time distributions
Jiwon Kim, Jonathan Corcoran
Persuasive scholarship presents how individual daily travel habits implicate congestion, environmental pollution, and the travel experience. However, the empirical characteristics…
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
Daehee Park, Hobin Ryu, Yunseo Yang +3
Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the obse…
A deep learning framework to generate realistic population and mobility data
Eren Arkangil, Mehmet Yildirimoglu, Jiwon Kim +1
Census and Household Travel Survey datasets are regularly collected from households and individuals and provide information on their daily travel behavior with demographic and econ…
Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches
Miner Zhong, Jiwon Kim, Zuduo Zheng
This paper addresses the problem of estimating link flows in a road network by combining limited traffic volume and vehicle trajectory data. While traffic volume data from loop det…
Some Reality Properties of Finite Simple Orthogonal Groups
Jiwon Kim, Stephen Trefethen, C. Ryan Vinroot
We prove several reality properties for finite simple orthogonal groups. For any prime power and , we show that all real conjugacy classes are strongly real in the sim…
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach
Zhixiong Jin, Jiwon Kim, Hwasoo Yeo +1
In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching…
TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning
Seongjin Choi, Jiwon Kim, Hwasoo Yeo
Recently, an abundant amount of urban vehicle trajectory data has been collected in road networks. Many studies have used machine learning algorithms to analyze patterns in vehicle…