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Pregled bibliografske jedinice broj: 1139680

Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions

Tišljarić, Leo; Ivanjko, Edouard; Kavran, Zvonko; Carić, Tonči
Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions // 14th International scientific conference on sustainable, modern and safe transport / Bujňák, Jan ; Guagliano, Mario (ur.).
Vysoké Tatry, Slovačka: Elsevier, 2021. str. 1389-1397 doi:10.1016/j.trpro.2021.07.124 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions

Autori
Tišljarić, Leo ; Ivanjko, Edouard ; Kavran, Zvonko ; Carić, Tonči

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
14th International scientific conference on sustainable, modern and safe transport / Bujňák, Jan ; Guagliano, Mario - : Elsevier, 2021, 1389-1397

Skup
14th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM)

Mjesto i datum
Vysoké Tatry, Slovačka, 26.05.2021. - 28.05.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass

Sažetak
The increased development of the urban areas consequently results in a larger number of vehicles on the road network, which inevitably leads to traffic congestion, especially in the rush hours. Intelligent transport systems solutions present the applications that can be useful in detecting and dealing with the problems that are related to congestion. This paper presents a method for the congestion index estimation using the speed transition matrix and the corresponding center of mass. The congestion index is estimated using a Fuzzy Inference System optimized by adopting the Genetic Algorithm. In this paper, the large real-world Global Navigation Satellite System data are used to evaluate the proposed method for the traffic state estimation of most relevant road segments in the largest city in Croatia, the City of Zagreb. The validation of results is performed using the domain knowledge presented in the Highway Capacity Manual, which resulted in the model’s precision of 94.6%. The result indicates a possible application of the method for the congestion estimation in urban centers.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport

Napomena
Transportation Research Procedia (Volume 55, 2021)



POVEZANOST RADA

Projekti:
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)

Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Tonči Carić (autor)

Avatar Url Zvonko Kavran (autor)

Avatar Url Edouard Ivanjko (autor)

Avatar Url Leo Tišljarić (autor)

Poveznice na cjeloviti tekst rada:

doi www.sciencedirect.com

Citiraj ovu publikaciju:

Tišljarić, Leo; Ivanjko, Edouard; Kavran, Zvonko; Carić, Tonči
Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions // 14th International scientific conference on sustainable, modern and safe transport / Bujňák, Jan ; Guagliano, Mario (ur.).
Vysoké Tatry, Slovačka: Elsevier, 2021. str. 1389-1397 doi:10.1016/j.trpro.2021.07.124 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Tišljarić, L., Ivanjko, E., Kavran, Z. & Carić, T. (2021) Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions. U: Bujňák, J. & Guagliano, M. (ur.)14th International scientific conference on sustainable, modern and safe transport doi:10.1016/j.trpro.2021.07.124.
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Ivanjko, Edouard and Kavran, Zvonko and Cari\'{c}, Ton\v{c}i}, year = {2021}, pages = {1389-1397}, DOI = {10.1016/j.trpro.2021.07.124}, keywords = {traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass}, doi = {10.1016/j.trpro.2021.07.124}, title = {Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions}, keyword = {traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass}, publisher = {Elsevier}, publisherplace = {Vysok\'{e} Tatry, Slova\v{c}ka} }
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Ivanjko, Edouard and Kavran, Zvonko and Cari\'{c}, Ton\v{c}i}, year = {2021}, pages = {1389-1397}, DOI = {10.1016/j.trpro.2021.07.124}, keywords = {traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass}, doi = {10.1016/j.trpro.2021.07.124}, title = {Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions}, keyword = {traffic congestion index speed frequency distribution speed transition matrix GNSS data center of mass}, publisher = {Elsevier}, publisherplace = {Vysok\'{e} Tatry, Slova\v{c}ka} }

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  • Scopus

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