Modeling

A Methodology for Evaluating the Performance of Model-Based Traffic Prediction Systems

Gomes, Gabriel
Gan, Qijian
Bayen, Alexandre M.
2018

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive...

Deep Truck : A Deep Neural Network Model for Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Chou, Fang-Chieh
Lu, Xiao-Yun
Bayen, Alexandre M.
2019

This article demonstrates the use of deep neural networks (NN) and deep reinforcement learning (deep-RL) for modeling and control of longitudinal heavy duty truck dynamics. Instead of explicit use of analytical model derived information or parameters about the truck, the deep NN model is fitted to data using a brief set of historical data collected from an arbitrary driving cycle. The deep model is used in this article to design a cruise controller for the truck using model-free deep-RL. The deep model and the control loop performances are demonstrated both using state-of-the-art...

Well-Posedness of Networked Scalar Semilinear Balance Laws Subject to Nonlinear Boundary Control Operators

Tang, Shu-Xia
Keimer, Alexander
Bayen, Alexandre M.
2019

Networked scalar semilinear balance laws are used as simplified macroscopic vehicular traffic models. The related initial boundary value problem is investigated, on a finite interval. The upstream boundary datum is determined by a nonlinear feedback control operator, representing the fact that traffic routing might be influenced in real time by the traffic information on the entire network. The main contribution of the present work lies in the appropriate design of nonlinear boundary control operators which meanwhile guarantee the well-posedness of the resultant systems. In detail, two...

Continual Learning of Microscopic Traffic Models Using Neural Networks

Farid, Yashar Zeinali
Kreidieh, Abdul Rahman
Khalighi, Farnoush
Lobel, Hans
Bayen, Alexandre M.
2021

In a mixed-autonomy traffic scenario, where human drivers and autonomous vehicles share the streets, self-driving cars need to be able to predict in a robust manner the behaviour of human-driven vehicles, in order to guarantee a safe and smooth driving experience. Although traffic theory provides several models of human drivers, these models are often parameterized by few parameters which can limit their performance in modeling complex behaviors. The lack of sufficient model capacity and the behavioral shifts in human driving reduces the usefulness of these methods in real-life...

Quasi-Dynamic Traffic Assignment using High Performance Computing

Chan, Cy
Kuncheria, Anu
Zhao, Bingyu
Cabannes, Theophile
Keimer, Alexander
Bayen, Alexandre
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...

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

Lee, Jonathan
Gunter, George
Ramadan, Rabie
Almatrudi, Sulaiman
Arnold, Paige
Work, Daniel B.
Seibold, Benjamin
Bayen, Alexandre
2021

This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the...

Boundary Controllability and Asymptotic Stabilization of a Nonlocal Traffic Flow Model

Bayen, Alexandre
Coron, Jean-Michel
De Nitti, Nicola
Keimer, Alexander
Pflug, Lukas
2021

We study the exact boundary controllability of a class of nonlocal conservation laws modeling traffic flow. The velocity of the macroscopic dynamics depends on a weighted average of the traffic density ahead and the averaging kernel is of exponential type. Under specific assumptions, we show that the boundary controls can be used to steer the system towards a target final state or out-flux. The regularizing effect of the nonlocal term, which leads to the uniqueness of weak solutions, enables us to prove that the exact controllability is equivalent to the existence of weak solutions to the...

Longitudinal Deep Truck: Deep Learning and Deep Reinforcement Learning for Modeling and Control of Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Wu, Trevor
Vurimi, Ganeshnikhil
Lu, Xiao-Yun
Bayen, Alexandre
2021

Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of expertise to carry through. This article investigates deep learning and deep reinforcement learning as truck-configuration-agnostic longitudinal modeling and control approaches for heavy duty trucks. The article outlines a process to develop and validate such models and...

Solving N-Player Dynamic Routing Games with Congestion: A Mean Field Approach

Cabannes, Theophile
Lauriere, Mathieu
Perolat, Julien
Marinier, Raphael
Girgin, Sertan
2021

The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows - applied in macroscopic and mesoscopic traffic modeling - the article introduces a new N-player dynamic routing game with explicit congestion dynamics. The model is well-posed and can reproduce heterogeneous departure times and congestion spill back phenomena. However, as Nash equilibrium computations are PPAD-complete, solving the game becomes intractable for large but realistic...

Multi-Adversarial Safety Analysis for Autonomous Vehicles

Bahati, Gilbert
Gibson, Marsalis
Bayen, Alexandre
2021

This work in progress considers reachability-based safety analysis in the domain of autonomous driving in multi-agent systems. We formulate the safety problem for a car following scenario as a differential game and study how different modelling strategies yield very different behaviors regardless of the validity of the strategies in other scenarios. Given the nature of real-life driving scenarios, we propose a modeling strategy in our formulation that accounts for subtle interactions between agents, and compare its Hamiltonian results to other baselines. Our formulation encourages...