Artificial Neural Network Study to Predict the Amount of Carried Weight by Rail Transportation System

Authors

  • NUR SYUHADA MUHAMMAT PAZIL FSKM UiTM CAWANGAN MELAKA KAMPUS JASIN

Keywords:

Artificial Neural Network (ANN), Conjugate Gradient Descent (CGD), Quasi-Newton (QN), Lavenberg-Marquardt (LM), algorithm

Abstract

Keretapi Tanah Melayu Berhad (KTMB) is the main rail operator in Peninsular Malaysia. KTMB provides cargo services which are safe, efficient and trustworthy. KTMB also has services that are connected to the port and inland port in Peninsular Malaysia. However, they remove suffered three major derailments in 2017. On November 23, a cargo train had an accident when 12 cargo trains traveling southward slipped between National Bank Station and Kuala Lumpur Station due to heavy weight and oversized loads carried by the cargo train. This study is conducted to predict the amount of carried weight of cargo by KTMB using Artificial Neural Network model. Datasets used in this study was taken from Department of Statistics Malaysia Official Portal from year 2001 to 2016. There are three algorithms chosen in this study which are Conjugate Gradient Descent (CGD), Quasi-Newton (QN) and Lavenberg-Marquardt (LM) algorithm. The best algorithm is selected to predict the amount of carried weight by comparing the value of error measures of the three algorithms which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Therefore, CGD is the best algorithm that produces smallest error of RMSE and MAPE. By using CGD algorithm, the results show the forecast value of carried weight for five years ahead which is from year 2017 until 2021 is decrease. 

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Published

2018-11-08

How to Cite

MUHAMMAT PAZIL, N. S. (2018). Artificial Neural Network Study to Predict the Amount of Carried Weight by Rail Transportation System. Journal of Computing Research and Innovation, 3(2), 17-23. Retrieved from https://crinn.conferencehunter.com/index.php/jcrinn/article/view/78

Issue

Section

General Computing