Traffic Operations and Management

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.

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...

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...