Traffic Operations and Management

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

Wu, Cathy
Kreidieh, Abdul Rahman
Parvate, Kanaad
Vinitsky, Eugene
Bayen, Alexandre M.
2022

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multivehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for...

Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

Almatrudi, Sulaiman
Parvate, Kanaad
Rothchild, Daniel
Vijay, Upadhi
2022

Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and...

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Lichtle, Nathan
Vinitsky, Eugene
Nice, Matthew
Seibold, Benjamin
Work, Dan
Bayen, Alexandre M.
2022

Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I–24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human...

Learning Energy-Efficient Driving Behaviors by Imitating Experts

Rahman Kreidieh, Abdul
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...

A Rigorous Multi-Population Multi-Lane Hybrid Traffic Model for Dissipation of Waves via Autonomous Vehicles

Kardous, Nicolas
Hayat, Amaury
McQuade, Sean T.
Gong, Xiaoqian
Truong, Sydney
Bayen, Alexandre
2022

In this paper, a multi-lane multi-population microscopic model, which presents stop-and-go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the traffic instabilities, and in particular stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on...

Reinforcement Learning Versus PDE Backstepping and PI Control for Congested Freeway Traffic

Yu, Huan
Park, Saehong
Bayen, Alexandre
2022

We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governed by a macroscopic Aw–Rascle–Zhang (ARZ) model, consisting of 2 \times 2 quasi-linear partial differential equations (PDEs) for traffic density and velocity. The boundary stabilization of the linearized ARZ PDE model has been solved by PDE backstepping, guaranteeing spatial L<sup>2</sup> norm regulation of the traffic state to uniform density and velocity and ensuring that traffic oscillations are...

A Rigorous Multi-Population Multi-Lane Hybrid Traffic Model for Dissipation of Waves via Autonomous Vehicles

Kardous, Nicolas
Hayat, Amaury
McQuade, Sean T.
Gong, Xiaoqian
Truong, Sydney
Bayen, Alexandre M.
2022

In this paper, a multi-lane multi-population microscopic model, which presents stop-and-go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the traffic instabilities, and in particular stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on...

Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

Diaz, Keith Anshilo
Dailisan, Damian
Sharaf, Umang
Santos, Carissa
Bayen, Alexander M.
2022

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low volume links for several cycles in order to optimize the network-level traffic flow. Here, we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings...

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset

Wu, Fangyu
Wang, Dequan
Hwang, Minjune
Hao, Chenhui
Lu, Jiawei
Bayen, Alexandre
2022

Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit...

Limitations and Improvements of the Intelligent Driver Model (IDM)

Albeaik, Saleh
Bayen, Alexandre
Chiri, Maria Teresa
Hayat, Amaury
Kardous, Nicolas
2022

Starting from interaction rules based on two levels of stochasticity we study the influence of the microscopic dynamics on the macroscopic properties of vehicular flow. In particular, we study the qualitative structure of the resulting flux-density and speed-density diagrams for different choices of the desired speeds. We are able to recover multivalued diagrams as a result of the existence of a one-parameter family of stationary distributions, whose expression is analytically found by means of a Fokker--Planck approximation of the initial Boltzmann-type model.