Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43966581" target="_blank" >RIV/49777513:23520/21:43966581 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.istiee.unict.it/sites/default/files/files/ET_2021_84_6.pdf" target="_blank" >http://www.istiee.unict.it/sites/default/files/files/ET_2021_84_6.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48295/ET.2021.84.6" target="_blank" >10.48295/ET.2021.84.6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Popis výsledku v původním jazyce

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

Název v anglickém jazyce

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Popis výsledku anglicky

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

European Transport / Trasporti Europei

ISSN

1825-3997

e-ISSN

—

Svazek periodika

84

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

000743358400001

EID výsledku v databázi Scopus

2-s2.0-85123735748