The project addresses open issues for Intelligent Transportation Systems (ITS). The goal of these systems is to use information technologies (sensing, signal processing, machine learning, communications, and control) to improve traffic flow, as well as enhance the safety and comfort of drivers. These tools are doubtlessly of very high societal and economical value. It has been established over the past several decades, through field studies and many scholarly publications, that the tools of ITS can significantly improve the flow of traffic on congested freeways and streets.

Three main objectives will be addressed in this collaboration:

Objective 1: Model validation and robust modeling for traffic estimation, control and forecasting. We propose to establish a collaborative effort to devise an integrated methodology for accomplishing sensor fault detection, reconfiguring the topology of the estimation and prediction modules of the traffic simulation systems and finally to that will permit reliable traffic state estimation on forecasting in freeway corridors systems.

Objective 2: New methods for traffic forecasting. The Associate team aims to:Derive, analyze, and validate robust stochastic traffic forecasting methods.Derive online estimation methods of stochastic parameters for both demand prediction and state reconstruction.

Objective 3: New methods for distributed traffic control and estimation. The goals are to study the traffic balancing approach in a joint control scheme including ramp metering and variable speed limit, study the distributed traffic approach for urban traffic and heterogeneous networks and develop new techniques for large scale estimation and control of freeway and arterial traffic states.