7 citations · 46 across the 16 of their papers we have counts for
18 papers
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…
Safety Perspective on Assisted Lane Changes: Insights from Open-Road, Live-Traffic Experiments
Konstantinos Mattas, Sandor Vass, Gergely Zachar +8
This study investigates the assisted lane change functionality of five different vehicles equipped with advanced driver assistance systems (ADAS). The goal is to examine novel, und…
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…
Stop-and-go wave super-resolution reconstruction via iterative refinement
Junyi Ji, Alex Richardson, Derek Gloudemans +6
Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technolo…
Field Deployment of Multi-Agent Reinforcement Learning Based Variable Speed Limit Controllers
Yuhang Zhang, Zhiyao Zhang, Marcos Quiñones-Grueiro +4
This article presents the first field deployment of a multi-agent reinforcement-learning (MARL) based variable speed limit (VSL) control system on the I-24 freeway near Nashville,…
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection
Austin Coursey, Junyi Ji, Marcos Quinones-Grueiro +5
Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event ident…
Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date
Mostafa Ameli, Sean Mcquade, Jonathan W. Lee +20
Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts…
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
Jonathan W. Lee, Han Wang, Kathy Jang +61
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,…
Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic
Han Wang, Zhe Fu, Jonathan Lee +14
This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was valid…
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kathy Jang, Nathan Lichtlé, Eugene Vinitsky +10
In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smoot…
Virtual trajectories for I-24 MOTION: data and tools
Junyi Ji, Yanbing Wang, Derek Gloudemans +3
This article introduces a new virtual trajectory dataset derived from the I-24 MOTION INCEPTION v1.0.0 dataset to address challenges in analyzing large but noisy trajectory dataset…
Online Min Cost Circulation for Multi-Object Tracking on Fragments
Yanbing Wang, Junyi Ji, William Barbour +1
Multi-object tracking (MOT) or global data association problem is commonly approached as a minimum-cost-flow or minimum-cost-circulation problem on a graph. While there have been n…
Enabling Mixed Autonomy Traffic Control
Matthew Nice, Matt Bunting, Alex Richardson +8
We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other no…
SAILing CAVs: Speed-Adaptive Infrastructure-Linked Connected and Automated Vehicles
Matthew Nice, Matthew Bunting, George Gunter +3
This work demonstrates a new capability in roadway control: Speed-adaptive, infrastructure-linked connected and automated vehicles. We develop and deploy a lightly modified vehicle…
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits
Yuhang Zhang, Marcos Quinones-Grueiro, Zhiyao Zhang +4
Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting spee…