Article ID Journal Published Year Pages File Type
5126982 Transportation Research Part B: Methodological 2017 24 Pages PDF
Abstract

•A framework for traffic state estimation on road networks is defined.•The framework leverages Hamilton Jacobi equations to solve estimation problems exactly.•Junctions modeling and entropy condition integration to junction flows are included.

Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton-Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton-Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.

Related Topics
Social Sciences and Humanities Decision Sciences Management Science and Operations Research
Authors
, ,