18 citations · 20 across the 9 of their papers we have counts for
7 papers · 1 filter
PMA-Diffusion: A Physics-guided Mask-Aware Diffusion Framework for TSE from Sparse Observations
Lindong Liu, Zhixiong Jin, Seongjin Choi
High-resolution highway traffic state information is essential for Intelligent Transportation Systems, but typical traffic data acquired from loop detectors and probe vehicles are…
Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting
Christopher Cheong, Gary Davis, Seongjin Choi
Spatiotemporal forecasting on transportation networks is a complex task that requires understanding how traffic nodes interact within a dynamic, evolving system dictated by traffic…
Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment
Donggyu Min, Seongjin Choi, Dong-Kyu Kim
This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.…
TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flows
Mitch Kosieradzki, Seongjin Choi
For intelligent transportation systems and autonomous vehicles to operate safely and efficiently, they must reliably predict the future motion and trajectory of surrounding agents…
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno +2
Large Language Models (LLMs) have recently demonstrated significant potential in time series forecasting, offering impressive capabilities in handling complex temporal data. Howeve…
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…
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
Sicong Jiang, Seongjin Choi, Lijun Sun
Cooperative Adaptive Cruise Control (CACC) plays a pivotal role in enhancing traffic efficiency and safety in Connected and Autonomous Vehicles (CAVs). Reinforcement Learning (RL)…