Traffic Operations and Management

Learning Energy-Efficient Driving Behaviors by Imitating Experts

Kreidieh, Abdul Rahman
Fu, Zhe
Bayen, Alexandre M.
2022

The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in...

Creating, Calibrating, and Validating Large-Scale Microscopic Traffic Simulation

Cabannes, Theophile
Bagabaldo, Alben Rome
Gan, Qianxin
Jain, Ayush
Blondel, Alice
Bayen, Alexandre M.
2023

The challenges of creating, calibrating, and validating a traffic microsimulation are not apparent until one tries to create their own. Through the development of a traffic microsimulation of the San Jose Mission district in Fremont, CA, this article shares a blueprint for creating, calibrating, and validating a large-scale microsimulation of any city. Codes and data are made openly available for anyone to reproduce the simulation or its creation inside the Aimsun microsimulator. The calibration process enables simulating the movement of 130,000 vehicles through a Fremont subnetwork...

Unified Automatic Control of Vehicular Systems With Reinforcement Learning

Yan, Zhongxia
Kreidieh, Abdul Rahman
Vinitsky, Eugene
Bayen, Alexandre M.
2023

Emerging vehicular systems with increasing proportions of automated components present opportunities for optimal control to mitigate congestion and increase efficiency. There has been a recent interest in applying deep reinforcement learning (DRL) to these nonlinear dynamical systems for the automatic design of effective control strategies. Despite conceptual advantages of DRL being model-free, studies typically nonetheless rely on training setups that are painstakingly specialized to specific vehicular systems. This is a key challenge to efficient analysis of diverse vehicular and...

Optimizing Mixed Autonomy Traffic Flow with Decentralized Autonomous Vehicles and Multi-Agent Reinforcement Learning

Vinitsky, Eugene
Lichtle, Nathan
Parvate, Kanaad
Bayen, Alexandre
2023

We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco–Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties...

Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments

Fu, Zhe
Kreidieh, Abdul Rahman
Wang, Han
Lee, Jonathan W.
Monache, Maria Laura Delle
Bayen, Alexandre M.
2023

Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings. In particular, when coupled with even modest traffic state estimates, such systems can plan and coordinate the behaviors of automated vehicles (AVs) in response to observed downstream events, thereby inhibiting the continued propagation of congestion. In this paper, we present a two-layer control strategy in which the upper layer proposes the desired speeds that predictively react to the downstream state of traffic, and the lower layer maintains safe and reasonable...

Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

Lichtle, Nathan
Jang, Kathy
Shah, Adit
Vinitsky, Eugene
Lee, Jonathan W.
Bayen, Alexandre M.
2023

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I–24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the...

Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

Khoudari, Nour
Almatrudi, Sulaiman
Ramadan, Rabie
Carpio, Joy
Yao, Mengsha
Butts, Kenneth
Bayen, Alexandre M.
2023

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate,...

Traffic Smoothing Using Explicit Local Controllers

Hayat, Amaury
Alanqary, Arwa
Bhadani, Rahul
Denaro, Christopher
Weightman, Ryan J.
Piccoli, Benedetto
Bayen, Alexandre M.
2023

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the...

Enabling Mixed Autonomy Traffic Control

Nice, Matthew
Bunting, Matthew
Richardson, Alex
Zachár, Gergely
Lee, Jonathan W.
Bayen, Alexandre
2023

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 non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable...

Optimal Control of Autonomous Vehicles for Flow Smoothing in Mixed-Autonomy Traffic

Alanqary, Arwa
Gong, Xiaoqian
Keimer, Alexander
Seibold, Benjamin
Piccoli, Benedetto
Bayen, Alexandre
2023

This article studies the optimal control of autonomous vehicles over a given time horizon to smooth traffic. We model the dynamics of a mixed-autonomy platoon as a system of non-linear ODEs, where the acceleration of human-driven vehicles is governed by a car-following model, and the acceleration of autonomous vehicles is to be controlled. We formulate the car-following task as an optimal control problem and propose a computational method to solve it. Our approach uses an adjoint formulation to compute gradients of the optimization problem explicitly, resulting in more accurate and...