Search Results for author:
Found 7 papers, 6 papers with code
Uncertainty-aware Traffic Prediction under Missing Data
However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations.
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.
Reinforcement Learning Approaches for Traffic Signal Control under Missing Data
In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them.
LibSignal: An Open Library for Traffic Signal Control
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks.
Modeling Network-level Traffic Flow Transitions on Sparse Data
DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process.
