Intelligent modelling with alternative approach: Application of advanced artificial intelligence into traffic management

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F17%3AA0000066" target="_blank" >RIV/47813059:19240/17:A0000066 - isvavai.cz</a>

Výsledek na webu

<a href="http://www3.uniza.sk/komunikacie/archiv/2017/4/4_2017en.pdf" target="_blank" >http://www3.uniza.sk/komunikacie/archiv/2017/4/4_2017en.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent modelling with alternative approach: Application of advanced artificial intelligence into traffic management

Popis výsledku v původním jazyce

The currently existing transport infrastructures are failing due to many problems. This paper deals with presenting a new approach of modelling and forecasting transport processes using artificial intelligence. Firstly, the current state of forecasting transport data is presented; the traditional as well as new artificial intelligence methods, such as artificial neural networks, are discussed and described. After that, a support vector regression prediction model is briefly presented and an empirical analysis is performed. Finally, on the basis of our experiment and performed comparative analysis we state that artificial intelligence (AI) intelligent methods have potential in the transport area as they can improve the efficiency, safety, and environmental compatibility of transport systems.

Název v anglickém jazyce

Intelligent modelling with alternative approach: Application of advanced artificial intelligence into traffic management

Popis výsledku anglicky

The currently existing transport infrastructures are failing due to many problems. This paper deals with presenting a new approach of modelling and forecasting transport processes using artificial intelligence. Firstly, the current state of forecasting transport data is presented; the traditional as well as new artificial intelligence methods, such as artificial neural networks, are discussed and described. After that, a support vector regression prediction model is briefly presented and an empirical analysis is performed. Finally, on the basis of our experiment and performed comparative analysis we state that artificial intelligence (AI) intelligent methods have potential in the transport area as they can improve the efficiency, safety, and environmental compatibility of transport systems.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Komunikacie

ISSN

1335-4205

e-ISSN

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Svazek periodika

19

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

7

Strana od-do

36-42

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85037697379