Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

2016 ◽  
Vol 26 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Majid Ghiasi ◽  
Nematollah Askarnejad ◽  
Saeid R. Dindarloo ◽  
Hamed Shamsoddini
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Open Pit ◽  
Open Pit Mines ◽  
Artificial Neural

scholarly journals Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

1970 ◽  
Vol 24 (1) ◽  
pp. 1-14
Author(s):  
Mustafa Özuysal ◽  
Gökmen Tayfur ◽  
Serhan Tanyel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Public Transportation ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Passenger Flow ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

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

Sign in / Sign up

Export Citation Format

Share Document