ITS Berkeley

Reconstruction of Boundary Conditions from Internal Conditions Using Viability Theory

Hofleitner, A.
Claudel, C.
Bayen, A.
2012

This article presents a method for reconstructing downstream boundary conditions to a HamiltonJacobi partial differential equation for which initial and upstream boundary conditions are prescribed as piecewise affine functions and an internal condition is prescribed as an affine function. Based on viability theory, we reconstruct the downstream boundary condition such that the solution of the Hamilton-Jacobi equation with the prescribed initial and upstream conditions and reconstructed downstream boundary condition satisfies the internal value condition. This work has important...

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.

Automatic Inference of Map Attributes from Mobile Data

Hofleitner, Aude
Côme, E.
Oukhellou, L.
Lebacque, Jean-Patrick
Bayen, A.
2012

The development and update of reliable Geographic Information Systems (GIS) greatly benefits Intelligent Transportation Systems developments including real-time traffic management platforms and assisted driving technologies. The collection and processing of the data required for the development and update of GIS is a long and expensive process which is prone to errors and inaccuracies, making its automation promising. The article introduces a method which leverages the emergence of sparsely sampled probe vehicle data to update and improve existing GIS. We present an unsupervised...

On Sequential Data Assimilation for Scalar Macroscopic Traffic Flow Models

Blandin, Sébastien
Couque, Adrien
Bayen, Alexandre
Work, Daniel
2012

We consider the problem of sequential data assimilation for transportation networks using optimal filtering with a scalar macroscopic traffic flow model. Properties of the distribution of the uncertainty on the true state related to the specific nonlinearity and non-differentiability inherent to macroscopic traffic flow models are investigated, derived analytically and analyzed. We show that nonlinear dynamics, by creating discontinuities in the traffic state, affect the performances of classical filters and in particular that the distribution of the uncertainty on the traffic state at...

Providing In-Vehicle Soft Safety Alerts Using Mobile Millennium Data and Vehicle Event Information

Nowakowski, Christopher
Gupta, Somak Datta
Myers, Scott
Shladover, Steven
Bayen, Alexandre
2012

The Mobile Millennium project provided a platform for aggregating traffic information across various sources, including infrastructure sensors, commercial data feeds, probe vehicles, and probe cell phones. The Networked Traveler project provided the California PATH instrumented research vehicle platform used to both deliver vehicle probe data back to the infrastructure and to generate Advanced Driver Assistance Systems (ADAS) alerts to the drivers of those vehicles. The main theme of this collaboration project was to demonstrate the potential to create Enhanced Probe Vehicles (EPVs) by...

A General Phase Transition Model for Traffic Flow on Networks

Blandin, Sébastien
Goatin, Paola
Piccoli, Benedetto
Bayen, Alexandre
Work, Daniel
2012

A general class of macroscopic traffic flow models describing traffic dynamics on transportation networks is presented, with emphasis on the formulation of the junction problem. The type of admissible waves generated at junctions under the formulation proposed and their impact on vehicle energy consumption are described.

Arterial Travel Time Forecast with Streaming Data: A Hybrid Approach of Flow Modeling and Machine Learning

Hofleitner, Aude
Herring, Ryan
Bayen, Alexandre
2012

This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile...

Mobile Phone Based Drifting Lagrangian Flow Sensors

Beard, Jonathan
Weekly, Kevin
Oroza, Carlos
Tinka, Andrew
Bayen, Alexandre M.
2012

Mobile phone based drifters offer distinct advantages over those using custom electronic circuit boards. They leverage the inexpensive and modern hardware provided by the mobile phone market to supply water resource scientists with a new solution to sensing water resources. Mobile phone based drifters strategically address in situ sensing applications in order to focus on the large scale use of mobile phones dealing with communications, software, hardware, and system reliability. We have demonstrated that a simple design of a drifter built around an Android phone robustly survives many...

Haas and ITS Fireside Chat - Powering the Future: Public-Private Partnerships at the Intersection of Mobility, Autonomy, and AI

May 9, 2025
Powering the Future: Public-Private Partnerships at the Intersection of Mobility, Autonomy, and AI

Probabilistic Formulation of Estimation Problems for a class of Hamilton-Jacobi Equations

Hofleitner, Aude
Claudel, Christian G.
Bayen, Alexandre M.
2012

This article presents a method for deriving the probability distribution of the solution to a Hamilton-Jacobi partial differential equation for which the value conditions are random. The derivations lead to analytical or semi-analytical expressions of the probability distribution function at any point in the domain in which the solution is defined. The characterization of the distribution of the solution at any point is a first step towards the estimation of the parameters defining the random value conditions. This work has important applications for estimation in flow networks in which...