Joint Flow and Density Reconstruction in Large Traffic Networks UsingPartial Turning Ratio Information - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1) , (1) , (1)
1

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.
Fichier principal
Vignette du fichier
CDC18_0292_FI.pdf (389.74 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01928699 , version 1 (21-11-2018)

Identifiers

Cite

Martin Rodriguez-Vega, Carlos Canudas de Wit, Hassen Fourati. Joint Flow and Density Reconstruction in Large Traffic Networks UsingPartial Turning Ratio Information. CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami, FL, United States. pp.205-210, ⟨10.1109/CDC.2018.8619321⟩. ⟨hal-01928699⟩
130 View
146 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More