S.Sarifah Radiah Shariff, Siti Nasuha Zubir, Wan Mazlina Wan Mohamed


Derailments of cargo train have frequently occurred during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. Severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. The major aim of the study is to model and predict the amount of carried weight of cargo train by routes in the rail transportation system, however, the unavailability of complete data is always a big problem. This study shows the process of handling missing values in the data. The data in carried cement in twelve (12) different routes by Keretapi Tanah Melayu Berhad (KTMB) cargo for 2016 -2018 is used as a case study.


Carried weight, cargo derailment, data imputation, missing data

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Bennett, D. A. (2001). How can I deal with missing data in my study? Australian And New Zealand Journal of Public Health, 25(5), 464-469.

Bernama. (2017, April 21). KTMB rekod 17 kemalangan maut babitkan pelanggaran tren pada 2018. Asto Awani, Retrieved -205358.

Bernama. (2017, November 23). The derailment disrupts KTM rail service. Malaymail, Retrieved -disrupts-ktm-rail-services/1516919.

Daim, N. (2019, April 20). Keep off the rail tracks, KTMB warn trespassers. The News Straits Times. Retrieved /keep-rail-tracks-ktmb-warn-trespassers.

Ghoushchi, S. J., & Rahman, M. N. A. (2016). Performance study of artificial neural network modelling to predict carried weight in the transportation system. International Journal of Logistics Systems and Management, 24(2), 200.

Hussin, M. H. (2019, July 18). Tren dijangka beroperasi semula Isnin. Mymetro, Retrieved

KTMB (2019). Network Service. Retrieved May 23, 2019, from

Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of clinical epidemiology, 110, 63-73.

Miyahara, H., Aihara, K., & Lechner, W. (2020). Quantum expectation-maximization algorithm. Physical Review A, 101(1), 012326.

Noor, H. M. (2017, August 28). Semua ‘tergelincir’ apabila kereta api keluar landasan. Utusan Online, Retrieved

Patil, B. M., Joshi, R. C., & Toshniwal, D. (2010, August). Missing value imputation based on k-mean clustering with weighted distance. In International Conference on Contemporary Computing (pp. 600-609). Springer, Berlin, Heidelberg.

Peng, L., & Lei, L. (2005). A review of missing data treatment methods. Intelligent Information Management Systems and Technologies, 1(3), 412-419.

Rahman, M. N. A., Jafarzadeh-Ghoushchi, S., Wahab, D. A., & Jafarzadeh-Ghoushji, M. (2014). Artificial Neural Network Modeling Studies to Predict the Amount Of Carried Weight By Iran Khodro Transportation System. Life Science Journal, 11(SPEC. ISS. 2), 146-154.

Schafer, J. L. (1999). Multiple imputation: a primer. Statistical methods in medical research, 8(1), 3-15.

van Ginkel, J. R., Linting, M., Rippe, R. C., & van der Voort, A. (2020). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. Journal of Personality Assessment, 102(3), 297-308.



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