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Title: Passenger flows estimation of light rail transit (LRT) system in Izmir, Turkey using multiple regression and ann methods
Other Titles: Çoklu regresyon ve yapay si̇ni̇r aǧları (YSA) yöntemleri̇ kullanılarak İzmi̇r-Türki̇ye'deki̇ hafi̇f rayli si̇steme (HRS) ai̇t yolcu akımlarının modellenmesi̇
Authors: Özuysal, Mustafa
Tayfur, Gökmen
Tanyel, Serhan
Keywords: Artificial neural networks
Light rail transit
Multiple regression
Public transportation
Issue Date: 2012
Publisher: Faculty of Transport and Traffic Sciences, University of Zagreb
Source: Özuysal, M., Tayfur, G., and Tanyel, S. (2012). Passenger flows estimation of light rail transit (LRT) system in İzmir, Turkey using multiple regression and ann methods. Promet - Traffic&Transportation, 24(1), 1-14.
Abstract: Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.
ISSN: 0353-5320
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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