Modeling

Decentralized Control of Conservation Laws on Graphs

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

Conservation and/or balance laws on networks in the recent years have been the subject of intense study, since a wide range of different applications in real life can be covered by such a research.

Decentralized Control of Conservation Laws on Graphs

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

Conservation and/or balance laws on networks in the recent years have been the subject of intense study, since a wide range of different applications in real life can be covered by such a research.

Distributed Control for Conservation Laws

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
2022

This chapter focuses on control of systems of conservation laws with distributed parameters. Problem with different parameterized fluxes is addressed: in particular, we deal with cases where the control is the maximal speed and look for continuous dependence of the solution on parameters.

Introduction

Bayen, Alexandre
Monache, Maria Laura Delle
Garavello, Mauro
Goatin, Paola
Piccoli, Benedetto
Bayen, Alexandre
2022

This book focuses on control problems for conservation laws, i.e., equations of the type: ∂tu+∂xf(u)=0ut+(f(u))x=0,$$\displaystyle \partial _t\, u + \partial _x\, f(u) = 0 \qquad u_t+(f(u))_x=0, $$where u:ℝ+×ℝ→ℝn$$u:\mathbb {R}^+\times \mathbb {R} \to \mathbb {R}^n$$is the vector of conserved quantities and f:ℝn→ℝn$$f:\mathbb {R}^n\to \mathbb {R}^n$$is the flux. Most results will be given for the scalar case (n = 1), but we will present few results valid in the general case.

Guest Editorial Special Issue on Modeling Dynamic Transportation Networks in the Age of Connectivity, Autonomy and Data

Savla, Ketan
Du, Lili
Samaranayake, Samitha
Ban, Xuegang Jeff
Bayen, Alexandre
2022

The recent emergence of new technologies and systems such as connected and automated vehicles (CAVs), novel incentive and routing platforms, and shared mobility services is making a significant impact on traffic flow in road networks. The rapid development of these innovations, powered by new capabilities in data collection, communication, and vehicle autonomy raises both great opportunities and new challenges for managing and controlling the transportation network efficiently. It is thus imperative to integrate the emerging systems into a dynamic transportation network analysis, and to...

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...

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...

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,...

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Lichtle, Nathan
Vinitsky, Eugene
Nice, Matthew
Bhadani, Rahul
Bunting, Matthew
2024

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient...

Car-Following Models: A Multidisciplinary Review

Zhang, Tianya Terry
Jin, Peter J.
McQuade, Sean T.
Bayen, Alexandre
Piccoli, Benedetto
2024

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help researchers to understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-following Models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, deep learning, and reinforcement learning. This paper presents an extensive...