%0 Generic %1 canepa2016networked %A Canepa, Edward S. %A Claudel, Christian G. %D 2016 %K transportation %T Networked Traffic State Estimation Involving Mixed Fixed-mobile Sensor Data Using Hamilton-Jacobi equations %U http://arxiv.org/abs/1606.03332 %X 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.

@misc{canepa2016networked, abstract = {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.}, added-at = {2017-10-06T17:47:38.000+0200}, author = {Canepa, Edward S. and Claudel, Christian G.}, biburl = {https://www.bibsonomy.org/bibtex/2050ff6424ae79c7ca3ca412429402117/masslabut}, description = {[1606.03332] Networked Traffic State Estimation Involving Mixed Fixed-mobile Sensor Data Using Hamilton-Jacobi equations}, interhash = {6bd93c135357babf09235c873b7dec58}, intrahash = {050ff6424ae79c7ca3ca412429402117}, keywords = {transportation}, note = {cite arxiv:1606.03332}, timestamp = {2017-10-06T17:47:38.000+0200}, title = {Networked Traffic State Estimation Involving Mixed Fixed-mobile Sensor Data Using Hamilton-Jacobi equations}, url = {http://arxiv.org/abs/1606.03332}, year = 2016 }