Traffic Theory

Heterogeneous Fleets of Active and Passive Floating Sensors for River Studies

Tinka, Andrew
Wu, Qingfang
Weekly, Kevin
Oroza, Carlos
Beard, Jonathan
Bayen, Alexandre
2016

Lagrangian sensing for tracing hydrodynamic trajectories is an innovative approach for studying estuarial environments. Actuated Lagrangian sensors are capable of avoiding obstacles and navigating when active and retain a passive hydrodynamic profile that is suited for Lagrangian sensing when passive. A heterogeneous fleet of actuated and passive drifting sensors is presented. Data assimilation using a high-performance computing (HPC) cluster that runs the ensemble Kalman filter (EnKF) is an essential component of the estuarial state estimation system. The performance of the mixed...

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...

Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies

Belletti, Francois
Sparks, Evan
Franklin, Michael J.
Bayen, Alexandre M.
Gonzalez, Joseph
2016

Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causal analysis operates in the time domain. Unfortunately, the direct application of time domain linear causal analysis to many real-world time series presents three critical challenges: irregular temporal sampling, long range dependencies, and scale. Moreover, real-world data is often collected at irregular time intervals across vast arrays of decentralized sensors and with long range dependencies which make naive...

On Learning How Players Learn: Estimation of Learning Dynamics in the Routing Game

Lam, Kiet
Krichene, Walid
Bayen, Alexandre M.
2016

The routing game models congestion in transportation networks, communication networks, and other cyber physical systems in which agents compete for shared resources. We consider an online learning model of player dynamics: at each iteration, every player chooses a route (or a probability distribution over routes, which corresponds to a flow allocation over the physical network), then the joint decision of all players determines the costs of each path, which are then revealed to the players. We pose the following estimation problem: given a sequence of player decisions and the corresponding...

Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games

Balandat, Maximilian
Krichene, Walid
Tomlin, Claire J.
Bayen, Alexandre
2016

We study a general version of the adversarial online learning problem. We are given a decision set $\mathcal{X}$ in a reflexive Banach space $X$ and a sequence of reward vectors in the dual space of $X$. At each iteration, we choose an action from $\mathcal{X}$, based on the observed sequence of previous rewards. Our goal is to minimize regret, defined as the gap between the realized reward and the reward of the best fixed action in hindsight. Using results from infinite dimensional convex analysis, we generalize the method of Dual Averaging (or Follow the Regularized Leader) to our...

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...

Creating Complex Congestion Patterns via Multi-Objective Optimal Freeway Traffic Control with Application to Cyber-Security

Reilly, Jack
Martin, Sébastien
Payer, Mathias
Bayen, Alexandre M.
2016

This article presents a study on freeway networks instrumented with coordinated ramp metering and the ability of such control systems to produce arbitrarily complex congestion patterns within the dynamical limits of the traffic system. The developed method is used to evaluate the potential for an adversary with access to control infrastructure to enact high-level attacks on the underlying freeway system. The attacks are executed using a predictive, coordinated ramp metering controller based on finite-horizon optimal control and multi-objective optimization techniques. The efficacy of the...

Filter Comparison for Estimation on Discretized PDEs Modeling Traffic: Ensemble Kalman Filter and Minimax Filter

Seo, Toru
Tchrakian, Tigran T.
Zhuk, Sergiy
Bayen, Alexandre M.
2016

Traffic State Estimation (TSE) refers to the estimation of the state (i.e., density, speed, or other parameters) of vehicular traffic on roads based on partial observation data (e.g., road-side detectors and on-vehicle GPS devices). It can be used as a component in applications such as traffic control systems as a means to identify and alleviate congestion. In existing studies, the Kalman Filter and its extensions, notably the Ensemble Kalman Filter (EnKF), are commonly employed for TSE. Recently, the MF has been newly adapted to this domain as a filtering algorithm for TSE. In this...

Traffic State Estimation on Highway: A Comprehensive Survey

Seo, Toru
Bayen, Alexandre
Kusakabe, Takahiko
Asakura, Yasuo
2017

Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a...

A Mathematical Framework for Delay Analysis in Single Source Networks

Samaranayake, Samitha
Parmentier, Axel
Xuan, Ethan
Bayen, Alexandre
2017

This article presents a mathematical framework for modeling heterogeneous flow networks with a single source and multiple sinks with no merging. The traffic is differentiated by the destination (i.e. Lagrangian flow) and different flow groups are assumed to satisfy the first-in-first-out (FIFO) condition at each junction. The queuing in the network is assumed to be contained at each junction node and spill-back to the previous junction is ignored. We show that this model leads to a well-posed problem for computing the dynamics of the system and prove that the solution is unique through a...