Joint Flow and Density Reconstruction in Large Traffic Networks Using Partial Turning Ratio Information

Martin Rodriguez-Vega 1 Carlos Canudas de Wit 1 Hassen Fourati 1
1 NECS - Networked Controlled Systems
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : We address the recent problem of state reconstruction in large scale traffic networks using heterogeneous sensor data. First, we deal with the conditions imposed on the number and location of fixed sensors such that all flows in the network can be uniquely reconstructed. We determine the minimum number of sensors needed to solve the problem given partial information of turning ratios, and then we propose a linear time algorithm for their allocation in a network. Using these results in addition to floating car data, we propose a method to reconstruct all traffic density and flow. Finally, the algorithms are tested in a simulated Manhattan-like network.
Type de document :
Communication dans un congrès
CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami, United States. IEEE, pp.1-6
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https://hal.inria.fr/hal-01928699
Contributeur : Hassen Fourati <>
Soumis le : mercredi 21 novembre 2018 - 09:34:00
Dernière modification le : vendredi 14 décembre 2018 - 01:10:58

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  • HAL Id : hal-01928699, version 1

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Martin Rodriguez-Vega, Carlos Canudas de Wit, Hassen Fourati. Joint Flow and Density Reconstruction in Large Traffic Networks Using Partial Turning Ratio Information. CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami, United States. IEEE, pp.1-6. 〈hal-01928699〉

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