4 citations · 8 across the 4 of their papers we have counts for
10 papers
Enabling Real-Time Phase Control in Traffic Signal Hardware-in-the-Loop Simulation
Zhiyao Zhang, Gergely Zachár, William Barbour +4
Advanced Traffic Signal Control (TSC) algorithms require real-time phase control, yet existing Hardware-in-the-Loop Simulation (HILS) testbeds only support pre-programmed timing pl…
Emission reduction potential of freeway stop-and-go wave smoothing
Junyi Ji, Derek Gloudemans, Gergely Zachár +4
The real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway waves requires opening a gap large enough for them to dissipate, but…
Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction
Junyi Ji, Derek Gloudemans, Gergely Zachár +3
The adaptive smoothing method (ASM) is a widely used approach for traffic state reconstruction. This article presents a Python implementation of ASM, featuring end-to-end calibrati…
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control System
Yuhang Zhang, Zhiyao Zhang, Junyi Ji +7
This article presents the first field deployment of a multi-agent reinforcement learning (MARL) based variable speed limit (VSL) control system on Interstate 24 (I-24) near Nashvil…
Scalable analysis of stop-and-go waves: Representation, measurements and insights
Junyi Ji, Derek Gloudemans, Yanbing Wang +5
Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of lar…
Phase Re-service in Reinforcement Learning Traffic Signal Control
Zhiyao Zhang, George Gunter, Marcos Quinones-Grueiro +4
This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of t…