Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorBaradaranshahidin, Milad
dc.contributor.authorShabani, Shahin
dc.contributor.authorKhalilzadeh, Mohammadreza
dc.contributor.authorTahmasbi Ashtiani, Saman
dc.contributor.authorTajaddini, Siavash
dc.contributor.authorAzimipour, Mohammad
dc.date.accessioned2023-01-30T11:00:32Z-
dc.date.available2023-01-30T11:00:32Z-
dc.date.issued2021
dc.identifier.citationBARADARANSHAHIDIN, M. SHABANI, S. KHALILZADEH, M. TAHMASBI ASHTIANI, S. TAJADDINI, S. AZIMIPOUR, M. Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach. European Transport / Trasporti Europei, 2021, roč. 84, č. 6, s. 1-16. ISSN: 1825-3997cs
dc.identifier.issn1825-3997
dc.identifier.uri2-s2.0-85123735748
dc.identifier.urihttp://hdl.handle.net/11025/51202
dc.format16 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherInstitute for Transport Studies in the European Economic Integrationen
dc.relation.ispartofseriesEuropean Transport / Trasporti Europeien
dc.rights© Institute for Transport Studies in the European Economic Integrationen
dc.titleModeling of BRT System Travel Time Prediction Using AVL Data and ANN Approachen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedImproving 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.en
dc.subject.translatedBus Rapid Transit (BRT)en
dc.subject.translatedTravel Time Predictionen
dc.subject.translatedArtificial Neural Network (ANN)en
dc.subject.translatedLinear Regressionen
dc.subject.translatedAutomatic Vehicle Location (AVL)en
dc.identifier.doi10.48295/ET.2021.84.6
dc.type.statusPeer-revieweden
dc.identifier.document-number743358400001
dc.identifier.obd43937581
dc.project.IDSGS-2022-027/Využití matematiky a informatiky v geomatice Vcs
Vyskytuje se v kolekcích:Články / Articles (KGM)
OBD

Soubory připojené k záznamu:
Soubor VelikostFormát 
ET_2021_84_6.pdf1,42 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/51202

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. OBD