3 papers
cs.LG2026
Online Estimation of Dynamic Origin-Destination Matrices Using Reinforcement Learning with Link-Flow Propagation Guidance
Donggyu Min, Dong-Kyu Kim
Dynamic origin-destination (OD) matrix estimation calibrates time-dependent input demand for simulations to reproduce observed link flows. Reinforcement learning is well suited to…
cs.LG2025
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.…
cs.LG2025
A Large Language Model for Feasible and Diverse Population Synthesis
Sung Yoo Lim, Hyunsoo Yun, Prateek Bansal +2
Generating a synthetic population that is both feasible and diverse is crucial for ensuring the validity of downstream activity schedule simulation in activity-based models (ABMs).…