Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v24i1.264
Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods
Mustafa Özuysal
Gökmen Tayfur
Serhan Tanyel
Sažetak
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.
Keywords: light rail transit, multiple regression, artificial neural networks, public transportation
Ključne riječi
light rail transit; multiple regression; artificial neural networks; public transportation
Hrčak ID:
82163
URI
Datum izdavanja:
1.1.2012.
Posjeta: 1.669 *