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Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services...

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Autores principales: Tišljarić, Leo, Vrbanić, Filip, Ivanjko, Edouard, Carić, Tonči
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003128/
https://www.ncbi.nlm.nih.gov/pubmed/35408421
http://dx.doi.org/10.3390/s22072807
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author Tišljarić, Leo
Vrbanić, Filip
Ivanjko, Edouard
Carić, Tonči
author_facet Tišljarić, Leo
Vrbanić, Filip
Ivanjko, Edouard
Carić, Tonči
author_sort Tišljarić, Leo
collection PubMed
description Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.
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spelling pubmed-90031282022-04-13 Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices Tišljarić, Leo Vrbanić, Filip Ivanjko, Edouard Carić, Tonči Sensors (Basel) Article Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms. MDPI 2022-04-06 /pmc/articles/PMC9003128/ /pubmed/35408421 http://dx.doi.org/10.3390/s22072807 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tišljarić, Leo
Vrbanić, Filip
Ivanjko, Edouard
Carić, Tonči
Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title_full Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title_fullStr Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title_full_unstemmed Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title_short Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
title_sort motorway bottleneck probability estimation in connected vehicles environment using speed transition matrices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003128/
https://www.ncbi.nlm.nih.gov/pubmed/35408421
http://dx.doi.org/10.3390/s22072807
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