Traffic Operations and Management

Quasi-Dynamic Traffic Assignment using High Performance Computing

Chan, Cy
Kuncheria, Anu
Zhao, Bingyu
Cabannes, Theophile
Keimer, Alexander
Wang, Bin
Alexandre Bayen
Macfarlane, Jane
2021

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is...

Introduction: Control Problems for Conservation Laws with Traffic Applications

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

Parallel Network Flow Allocation in Repeated Routing Games via LQR Optimal Control

Gibson, Marsalis
You, Yiling
Alexandre Bayen
2021

In this article, we study the repeated routing game problem on a parallel network with affine latency functions on each edge. We cast the game setup in a LQR control theoretic framework, leveraging the Rosenthal potential formulation. We use control techniques to analyze the convergence of the game dynamics with specific cases that lend themselves to optimal control. We design proper dynamics parameters so that the conservation of flow is guaranteed. We provide an algorithmic solution for the general optimal control setup using a multiparametric quadratic programming approach (explicit MPC...

Distributed Control for Conservation Laws

Alexandre Bayen
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.

Lagrangian Control of Conservation Laws and Mixed Models

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

A vehicle with different (eventually controlled) dynamics from general traffic along a street may reduce the road capacity, thus generating a moving bottleneck, and can be used to act on the traffic flow. The interaction between the controlled vehicle and the surrounding traffic, and the consequent flow reduction at the bottleneck position, can be described either by a conservation law with space dependent flux function [200], or by a strongly coupled PDE-ODE system proposed in [112, 208].

Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic

Kreidieh, Abdul Rahman
Zhao, Yibo
Parajuli, Samyak
Alexandre Bayen
2021

Reinforcement learning techniques can provide substantial insights into the desired behaviors of future autonomous driving systems. By optimizing for societal metrics of traffic such as increased throughput and reduced energy consumption, such methods can derive maneuvers that, if adopted by even a small portion of vehicles, may significantly improve the state of traffic for all vehicles involved. These methods, however, are hindered in practice by the difficulty of designing efficient and accurate models of traffic, as well as the challenges associated with optimizing for the behaviors of...

A Holistic Approach to the Energy-Efficient Smoothing of Traffic via Autonomous Vehicles

Hayat, Amaury
Gong, Xiaoqian
Lee, Jonathan
Truong, Sydney
McQuade, Sean T.
Alexandre Bayen
2022

The technological advancements in terms of vehicle on-board sensors and actuators, as well as for infrastructures, open an unprecedented scenario for the management of vehicular traffic. We focus on the problem of smoothing traffic by controlling a small number of autonomous vehicles immersed in the bulk traffic stream. Specifically, we aim at dissipating stop-and-go waves, which are ubiquitous and proven to increase fuel consumption tremendously and reduce. Our approach is holistic, as it is based on a large collaborative effort, which ranges from mathematical models for traffic and...

Control Problems for Hamilton-Jacobi Equations Co-authored by Alexander Keimer

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

In this chapter, we introduce Hamilton-Jacobi PDEs. These PDEs are related to conservation laws and their solutions are the anti-derivative (in space) of the Entropy solutions of the corresponding conservation law, given that some assumptions are satisfied.

Medium-Scale to Large-Scale Implementation of Cyber-Physical Human Experiments in Live Traffic

McQuade, Sean T.
Denaro, Christopher
Mahmood, Malaika
Lee, Jonathan W.
Gumm, Gracie
Alexandre Bayen
2022

Autonomous Vehicles (AVs) such as cars and trucks are being developed and tested as Cyber-Physical Human systems while the technology improves. Before these systems can achieve full autonomy, some serve as tools in the form of adaptive cruise control. The CIRCLES Consortium investigates the potential for AVs to increase fuel efficiency of highway traffic by smoothing “stop-and-go” traffic waves that result from normal human driving behavior in congestion. We have performed an experiment to evaluate the real world effects of implementing this strategy. A medium-scale experiment was...