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 |
Files in This Item:
File | Description | Size | Format | |
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AppliedSoftComputing202301.pdf | 1.53 MB | Adobe PDF | View/Open |
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