Traffic Operations and Management

Scalable Learning of Segment-Level Traffic Congestion Functions

Choudhury, Shushman
Kreidieh, Abdul Rahman
Tsogsuren, Iveel
Arora, Neha
Osorio, Carolina
Bayen, Alexandre M.
2024

We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area. First, we pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. Second, we train a feed-forward neural network on this dataset, which we can then use on any...

Improving Social Cost in Traffic Routing with Bounded Regret via Second-Best Tolls

Alanqary, Arwa
Kreidieh, Abdul Rahman
Samitha Samaranayake
Bayen, Alexandre M.
2024

In this work, we investigate algorithmic improvements that navigation services can implement to steer road networks toward a system-optimal state while retaining high levels of user compliance. We model the compliance of users using marginal regret, and we extend the definition of the social cost function to account for various traffic congestion externalities. We propose a routing algorithm for the static traffic assignment problem that improves the social cost with guarantees on the worst-case regret in the network. This algorithm leverages the connection we establish between this...

Stability of Ring Roads and String Stability of Car Following Models

Chou, Fang-Chieh
Keimer, Alexander
Bayen, Alexandre M.
2024

The ring road, a closed circular track, provides a controlled environment for studying car-following behavior in dynamic traffic flow. This work examines the ring road from a stability analysis perspective to shed light on its underlying stability mechanisms. We model the ring road as an interconnected system composed of subsystems of ODEs which can represent car-following dynamics. We analyze its stability based on the string stability of these subsystems and then apply the findings to ring road models. Our study addresses both interconnected systems consisting of homogeneous (identical)...

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

Ameli, Mostafa
McQuade, Sean T.
Lee, Jonathan W.
Bunting, Matthew
Nice, Matthew
Wang, Han
Barbour, William
Weightman, Ryan J.
Denaro, Christopher
Delorenzo, Ryan
Hornstein, Sharon
Davis, Jon F.
Timsit, Dan
Wagner, Riley
Xu, Ruotong
Mahmood, Malaika
Mahmood, Mikail
Monache, Maria Laura Delle
Seibold, Benjamin
Work, Daniel
Sprinkle, Jonathan M.
Piccoli, Benedetto
Bayen, Alexandre M.
2025

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article...

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

Lee, Jonathan W.
Wang, Han
Jang, Kathy
Lichtle, Nathan
Hayat, Amaury
Bunting, Matthew
Alanqary, Arwa
Barbour, William
Fu, Zhe
Gong, Xiaoqian
Gunter, George
Hornstein, Sharon
Kreidieh, Abdul Rahman
Nice, Mat-Thew W.
Richardson, William A.
Shah, Adit
Vinitsky, Eugene
Wu, Fangyu
Xiang, Shengquan
Almatrudi, Sulaiman
Althukair, Fahd
Bhadani, Rahul
Carpio, Joy
Chekroun, Raphael
Cheng, Eric
Chiri, Maria Teresa
Chou, Fang-Chieh
Delorenzo, Ryan
Gibson, Marsalis
Gloudemans, Derek A.
Gollakota, Anish
Ji, Junyi
Keimer, Alexander
Khoudari, Nour
Mahmood, Malaika
Mahmood, Mikail
Matin, Hossein Nick Zinat
McQuade, Sean T.
Ramadan, Rabie
Urieli, Daniel
Wang, Xia
Wang, Yanbing
Xu, Rita
Yao, Mengsha
You, Yiling
Zachár, Gergely
Zhao, Yibo
Ameli, Mostafa
Baig, Mirza Najamuddin
Bhaskaran, Sarah
Butts, Kenneth
Gowda, Manasi
Janssen, Caroline
Lee, John
Pedersen, Liam
Wagner, Riley
Zhang, Zimo
Zhou, Chang
Work, Daniel B.
Seibold, Benjamin
Sprinkle, Jonathan M.
Piccoli, Benedetto
Monache, Maria Laura Delle
Bayen, Alexandre M.
2025

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller...

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Wang, Han
Fu, Zhe
Lee, Jonathan W.
Matin, Hossein Nick Zinat
Alanqary, Arwa
Urieli, Daniel
Hornstein, Sharon
Kreidieh, Abdul Rahman
Chekroun, Raphael
Barbour, William
Richardson, William A.
Work, Dan
Piccoli, Benedetto
Seibold, Benjamin
Sprinkle, Jonathan M.
Bayen, Alexandre M.
Monache, Maria Laura Delle
2025

This article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The...

Modeling, Monitoring, and Controlling Road Traffic Using Vehicles to Sense and Act

Monache, Maria Laura Delle
McQuade, Sean T.
Matin, Hossein Nick Zinat
Gloudemans, Derek A.
Wang, Yanbing
Gunter, George L.
Bayen, Alexandre M.
Lee, Jonathan W.
Piccoli, Benedetto
Seibold, Benjamin
Sprinkle, Jonathan M.
Work, Daniel B.
2025

This review offers a comprehensive overview of current traffic modeling, estimation, and control methods, along with resulting field experiments. It highlights key developments and future directions in leveraging technological advancements to improve traffic management and safety. The focus is on macroscopic, microscopic, and micro-macro models, as well as state-of-the-art control techniques and estimation methods for deploying vehicles in traffic field experiments.