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PASSENGER FLOWS ESTIMATION OF LIGHT RAIL TRANSIT (LRT) SYSTEM IN IZMIR, TURKEY USING MULTIPLE REGRESSION AND ANN METHODS

Access

info:eu-repo/semantics/openAccess

Date

2012

Author

Ozuysal, Mustafa
Tayfur, Gokmen
Tanyel, Serhan

Metadata

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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.

Source

Promet-Traffic & Transportation

Volume

24

Issue

1

URI

https://doi.org/10.7307/ptt.v24i1.264
https://hdl.handle.net/11147/10565

Collections

  • Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection [4673]
  • WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection [4803]



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