Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1292
Title: Structured prediction of sparse dependent variables for traffic state estimation in large-scale networks
Authors: Petrović, Andrija
Radovanović, Sandro
Nikolić, Mladen 
Delibašić, Boris
Jovanović, Miloš
Affiliations: Informatics and Computer Science 
Keywords: Classification;Conditional random fields;Large-scale networks;Structured regression;Traffic state estimation
Issue Date: 1-Jan-2023
Rank: M21a
Publisher: Elsevier
Journal: Applied Soft Computing
Abstract: 
Currently, one of the biggest challenges in modern traffic engineering is related to traffic state estimation (TSE). Although many machine learning and domain models can be used for TSE, they do not consider the sparsity and spatial dependence of traffic state variables. In this paper, we propose a hybrid soft computing model of two Gaussian conditional random field (GCRF) models for the inference...
URI: https://research.matf.bg.ac.rs/handle/123456789/1292
ISSN: 15684946
DOI: 10.1016/j.asoc.2022.109893
Rights: Attribution-NonCommercial-NoDerivs 3.0 United States
Appears in Collections:Research outputs

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