A hybrid model for forecasting the volume of passenger flows on Serbian railways

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F15%3A39899860" target="_blank" >RIV/00216275:25510/15:39899860 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s12351-015-0198-5" target="_blank" >http://dx.doi.org/10.1007/s12351-015-0198-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12351-015-0198-5" target="_blank" >10.1007/s12351-015-0198-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid model for forecasting the volume of passenger flows on Serbian railways

Popis výsledku v původním jazyce

The accuracy of predicting the volume of railway passenger flows is very significant because of the vital role in the basic functions of transportation resources management. Although dealing with this problem is very often based on the use of the neuralnetworks, the uncertainty which dominates in the functioning of transportation systems is of great significance. The neural networks have been used for the time-series prediction with good results. This research compared two methods the parametric and the non-parametric approach. This study aims at presenting a hybrid model based on the integration of the genetic algorithm (GA) and the artificial neural networks (ANN) for forecasting the monthly volume of passengers on the Serbian railways. This innovative hybrid demonstrates how the genetic algorithms can be used to optimize the network architecture. By applying the idea of genetic algorithms in the neural networks, the integration is used so that on the basis of the input data, the se

Název v anglickém jazyce

A hybrid model for forecasting the volume of passenger flows on Serbian railways

Popis výsledku anglicky

The accuracy of predicting the volume of railway passenger flows is very significant because of the vital role in the basic functions of transportation resources management. Although dealing with this problem is very often based on the use of the neuralnetworks, the uncertainty which dominates in the functioning of transportation systems is of great significance. The neural networks have been used for the time-series prediction with good results. This research compared two methods the parametric and the non-parametric approach. This study aims at presenting a hybrid model based on the integration of the genetic algorithm (GA) and the artificial neural networks (ANN) for forecasting the monthly volume of passengers on the Serbian railways. This innovative hybrid demonstrates how the genetic algorithms can be used to optimize the network architecture. By applying the idea of genetic algorithms in the neural networks, the integration is used so that on the basis of the input data, the se

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JO - Pozemní dopravní systémy a zařízení

OECD FORD obor

—

Projekt

<a href="/cs/project/EE2.3.30.0058" target="_blank" >EE2.3.30.0058: Rozvoj kvalitních vědeckovýzkumných týmů na Univerzitě Pardubice</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Operational Research

ISSN

1109-2858

e-ISSN

—

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

17.9. 2015

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84940914329