Roads/Highways

Real-Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

Samaranayake, Samitha
Glaser, Steven D.
Holstius, David
Monteil, Julien
Tracton, Ken
Bayen, Alexandre
2014

Traffic-related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic-related air pollution remains a challenge. Obtaining high-resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the...

Adjoint-Based Optimization on a Network of Discretized Scalar Conservation Laws with Applications to Coordinated Ramp Metering

Reilly, Jack
Samaranayake, Samitha
Delle Monache, Maria
Krichene, Walid
Goatin, Paola
Bayen, Alexandre M.
2015

The adjoint method provides a computationally efficient means of calculating the gradient for applications in constrained optimization. In this article, we consider a network of scalar conservation laws with general topology, whose behavior is modified by a set of control parameters in order to minimize a given objective function. After discretizing the corresponding partial differential equation models via the Godunov scheme, we detail the computation of the gradient of the discretized system with respect to the control parameters and show that the complexity of its computation scales...

Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

Allström, Andreas
Ekström, Joakim
Gundlegård, David
Ringdahl, Rasmus
Rydergren, Clas
Bayen, Alexandre M.
Patire, Anthony D.
2016

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches...

Characterization of the Convective Instability of the Aw-Rascle-Zhang Model via Spectral Analysis

Belletti, Francois
Huo, Mandy
Litrico, Xavier
Bayen, Alexandre M.
2016

This article starts from the classical Aw-Rascle-Zhang (ARZ) model for freeway traffic and develops a spectral analysis of its linearized version. A counterpart to the Froude number in hydrodynamics is defined that enables a classification of the nature of vehicle traffic flow using the explicit solution resulting from the analysis. We prove that our linearization about an equilibrium is stable for congested regimes and convective-unstable otherwise. NGSIM data for congested traffic trajectories is used to compare the linearized model's predictions with actual macroscopic behavior of...

Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

Belletti, Francois
Haziza, Daniel
Gomes, Gabriel
Bayen, Alexandre M.
2018

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid...

Boundary Observer for Congested Freeway Traffic State Estimation via Aw-Rascle-Zhang model

Yu, Huan
Bayen, Alexandre M.
Krstic, Miroslav
2019

This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a boundary observer design so that estimates of aggregated traffic states in a freeway segment are obtained simply from boundary measurement of flow and velocity. The macroscopic traffic dynamics is...

An Equitable and Integrated Approach to Paying for Roads in a Time of Rapid Change

Bayen, Alexandre
Shaheen, Susan
Forscher, Edward H.
Lazarus, Jessica
2019

A brief overview of transportation user fees (historically and in a contemporary context) is presented followed by a discussion on how segmenting travel into three categories –long haul, the last mile, and at the curb–creates a new typology for transportation pricing and access mechanisms....

Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control

Lichtle, Nathan
Vinitsky, Eugene
Gunter, George
Velu, Akash
Bayen, Alexandre M.
2021

In this work, we demonstrate optimization of fuel economy in a large, calibrated model of a portion of the Ventura Freeway using a low penetration rate of controlled autonomous vehicles. We create waves in this network using a string-unstable car-following model and introduce a ghost cell to allow waves to propagate out of the network. Using multi-agent reinforcement learning, we then design a controller that manages to partially dampen the waves and thus increase the average energy efficiency of the system, yielding a 25% fuel consumption reduction at a 10% penetration rate. Finally, we...

The I-24 Trajectory Dataset

Nice, Matthew
Lichtle, Nathan
Gumm, Gracie
Roman, Michael
Vinitsky, Eugene
Elmadani, Safwan
Bayen, Alexandre
2021

This dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes values for Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS and more. This empirical dataset is useful for understanding/simulating real vehicle trajectories and vehicle controller performance.

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...