Artificial neural network modeling studies to predict the amount of carried weight by rail transportation system / Nur Syuhada Muhammat Pazil, Siti Nor Nadrah Muhamad and Hanis Syazana Nor Azahar

• Main Authors: Muhammat Pazil, Nur Syuhada, Muhamad, Siti Nor Nadrah, Nor Azahar, Hanis Syazana
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2018
• Subjects: Neural networks (Computer science); Algorithms
• Online Access: https://ir.uitm.edu.my/id/eprint/55253/1/55253.pdf; https://ir.uitm.edu.my/id/eprint/55253/; https://crinn.conferencehunter.com/
• Institution: Universiti Teknologi Mara

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 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.