Modeling

Inverse Modeling for Open Boundary Conditions in Channel Network

Wu, Qingfang
Rafiee, Mohammad
Tinka, Andrew
Bayen, Alexandre M.
2009

An inverse modeling problem for systems of networked one dimensional shallow water equations subject to periodic forcing is investigated. The problem is described as a PDE-constrained optimization problem with the objective of minimizing the norm of the difference between the observed variables and model outputs. After linearizing and discretizing the governing equations using an implicit discretization scheme, linear constraints are constructed which leads to a quadratic programming formulation of the state estimation problem. The usefulness of the proposed approach is illustrated with a...

A Class of Perturbed Cell-Transmission Models to Account for Traffic Variability

Blandin, Sébastien
Work, Daniel
Goatin, Paola
Piccoli, Benedetto
Bayen, Alexandre M.
2010

We introduce a general class of traffic models derived as perturbations of cell-transmission type models. These models use different dynamics in free-flow and in congestion phases. They can be viewed as extensions to cell transmission type models by considering the velocity to be a function not only of the density but also of a second state variable describing perturbations. We present the models in their discretized form under a new formulation similar to the classical supply demand formulation used by the seminal Cell-Transmission Model. We then show their equivalence to hydrodynamic...

Feed-Forward Control of Open Channel Flow Using Differential Flatness

Rabbani, Tarek
Meglio, Florent Di
Litrico, Xavier
Bayen, Alexandre M.
2010

This brief derives a method for open-loop control of open channel flow, based on the Hayami model, a parabolic partial differential equation resulting from a simplification of the Saint-Venant equations. The open-loop control is represented as infinite series using differential flatness, for which convergence is assessed. A comparison is made with a similar problem available in the literature for thermal systems. Numerical simulations show the effectiveness of the approach by applying the open-loop controller to irrigation canals modeled by the full Saint-Venant equations.

A Dual Decomposition Method for Sector Capacity Constrained Traffic Flow Optimization

Sun, Dengfeng
Clinet, Alexis
Bayen, Alexandre M.
2011

An aggregate air traffic flow model based on a multicommodity network is used for traffic flow management in the National Airspace System. The problem of minimizing the total travel time of flights in the National Airspace System of the United States, subject to sector capacity constraints, is formulated as an Integer Program. The resulting solution achieves optimal delay control. The Integer Program implemented for the scenarios investigated has billions of variables and constraints. It is relaxed to a Linear Program for computational efficiency. A dual decomposition method is...

Optimal Decomposition of Travel Times Measured by Probe Vehicles Using a Statistical Traffic Flow Model

Hofleitner, A.
Bayen, A.
2011

Sparse location measurements of probe vehicles are a promising data source for arterial traffic monitoring. One common challenge in processing this source of data is that vehicles are sampled infrequently (on the order of once per minute), which means that many vehicles will travel several links of the network between consecutive measurements. In this article, we propose an optimal decomposition of path travel times of probe vehicles to link travel times for each link traversed. From a model of arterial traffic dynamics, we derive probability distributions of travel times. We prove that...

Learning the Dependency Structure of Highway Networks for Traffic Forecast

Samaranayake, Samitha
Blandin, Sébastien
Bayen, Alexandre
2011

Forecasting road traffic conditions requires an accurate knowledge of the spatio-temporal dependencies of traffic flow in transportation networks. In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast. We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm to identify the inclusion-optimal dependency structure of the network given historical observations. The optimal dependency structure learned by the proposed algorithm is evaluated on...

Analytical and Grid-Free Solutions to the Lighthill–Whitham–Richards Traffic Flow Model

Mazaré, Pierre-Emmanuel
Dehwah, Ahmad H.
Claudel, Christian G.
Bayen, Alexandre M.
2011

In this article, we propose a computational method for solving the Lighthill–Whitham–Richards (LWR) partial differential equation (PDE) semi-analytically for arbitrary piecewise-constant initial and boundary conditions, and for arbitrary concave fundamental diagrams. With these assumptions, we show that the solution to the LWR PDE at any location and time can be computed exactly and semi-analytically for a very low computational cost using the cumulative number of vehicles formulation of the problem. We implement the proposed computational method on a representative traffic flow scenario...

Three-Stream Model for Arterial Traffic

Bails, Constant
Hofleitner, Aude
Xuan, Yiguang
Bayen, Alexandre M.
2012

In this article, a new analytical traffic flow model is proposed for traffic dynamics at signalized intersections. During each cycle, both the arrival and the departure traffic are approximated by three distinct traffic streams with uniform density. Because of the similar representation of the arrival and the departure traffic, results from a single intersection can easily be extended to a series of intersections. With this model, the number of parameters of the model is tractable, leading to analytical solutions of the problem. It also is proved that the total delay of one-way...

Large-Scale Estimation of Arterial Traffic and Structural Analysis of Traffic Patterns from Probe Vehicles

Hofleitner, Aude
Herring, Ryan
Bayen, Alexandre
Han, Yufei
2012

Estimating and analyzing traffic conditions on large arterial networks is an inherently difficult task. The first goal of this article is to demonstrate how arterial traffic conditions can be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage of vehicles. Traffic signals, stop signs, and other flow inhibitors make estimating arterial traffic conditions significantly more difficult than estimating highway traffic conditions. To address these challenges, a statistical modeling framework is proposed that leverages a large historical database and...

State Estimation in Large-Scale Open Channel Networks Using Particle Filters

Rafiee, Mohammad
Barrau, Axel
Bayen, Alexandre
2012

We consider the problem of estimating flow state in real time in large-scale open channel networks. After constructing a state space model of the flow based on the Saint-Venant equations, we implement the optimal sequential importance resampling (SIR) filter to perform state estimation using some additional flow measurements. The estimation method is implemented using a model of a network of 19 subchannels and one reservoir, Clifton Court Forebay, in Sacramento-San Joaquin Delta in California and the numerical results are presented.