MODEL ESTIMASI BANGKITAN PERGERAKAN MODA LAUT MENGGUNAKAN METODE REGRESI LINIER DAN RANDOM FOREST

Fadhlullah Apriandy, Sugiarto Sugiarto, Sofyan M Saleh, Lulusi Lulusi

Abstract


The need for people and goods movement across regions have been increasing susbtantially over the years. Sea transportations plays pivotal role in allowing this trans-national movement.The ferry line connecting Langsa, Indonesia and Penang, Malaysia had been established to help people and good across borders, albeit no longer operating. This study aims to estimate trip generation and attraction between these ports and their surrounding areas, and suggest whether this ferry line should reoperate. In doing so, random forest was utilised alongside linear regression model. Random forest has been increasingly popular among researchers in making estimates or preditions, whether calculating numerical or categorical data. To develop both models, historical trip and socio-economics data were employed. It was found that Gross Domestic Regional Product (GDRP) and population significantly affect trip generation and attraction. Both variables shows positive effect on trip generation and attraction: an increase in either should increase trip generation and attraction. Similar to linear regression model, random forest model show an excellent predictive capability. With a sophisticated model incorporating more variables, it is expected that random forest should well exceed linear regression predicitve capability. As population and GDRP are expected to continue to grow, it is suggested that stakeholders should reoperate this Langsa-Penang ferry line.

Keywords


Trip generation; Trip attraction; Sea transportation; Linear regression; Random forest; Socio-demographics

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References


Aghaabbasi, M., Shekari, Z. A., Shah, M. Z., Olakunle, O., Armaghani, D. J., Moeinaddini, M. 2020. Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques. Transportation Research Part A: Policy and Practice, 136, pp. 262-281.

Alekseev, K. P. G., Seixas, J. M. 2009. A multivariate neural forecasting modeling for air transport–preprocessed by decomposition: a Brazilian application. Journal of Air Transport Management, 15(5), pp. 212-216.

Barua, L., Zou, B., Zhou, Y. 2020. Machine learning for international freight transportation management: a comprehensive review. Research in Transportation Business & Management. 34, p. 100453.

BPS Langsa. 2020. Langsa Municipality in Figures 2020. BPS Langsa, Langsa, Indonesia.

Breiman, L. 2001. Random forests. Machine learning, 45(1), pp. 5-32.

Cheng, L., Chen, X., De Vos, J., Lai, X., Witlox, F. 2019. Applying a random forest method approach to model travel mode choice behavior. Travel Behaviour and Society, 14, pp. 1-10.

Cordón-Lagares, E., García-Ordaz, F. 2020. Factors affecting the survival of maritime goods transport firms in Spain. Research in Transportation Business & Management. 37, p. 100520.

Grant, D. B., Elliott, M. 2018. A proposed interdisciplinary framework for the environmental management of water and air-borne emissions in maritime logistics. Ocean & coastal management, 163, pp.162-172.

Hagenauer, J., Helbich, M. 2017. A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, pp. 273-282.

Profillidis, V. A., Botzoris, G. N. 2019. Modeling of Transport Demand. Elsevier, Oxford, UK.

Tournadre, J. 2014. Anthropogenic pressure on the open ocean: The growth of ship traffic revealed by altimeter data analysis. Geophysical Research Letters, 41(22), pp.7924-7932.

Tsui, W. H., Fung, M. K. 2016. Analysing passenger network changes: The case of Hong Kong. Journal of Air Transport Management, 50, pp. 1-11.

Yan, X., Liu, X., Zhao, X. 2020. Using machine learning for direct demand modeling of ridesourcing services in Chicago. Journal of Transport Geography, 83, p.102661.

Zhao, X., Yan, X., Yu, A., Van Hentenryck, P. 2020. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel behaviour and society. 20, pp. 22-35.




DOI: https://doi.org/10.24815/jarsp.v4i4.25844

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Copyright (c) 2021 Fadhlullah Apriandy, Sugiarto Sugiarto, Sofyan M Saleh, Lulusi Lulusi

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Jurnal Arsip Rekayasa Sipil dan Perencanaan (Journal of Archive in Civil Engineering and Planning).

Terakreditasi SINTA 3 Berdasarkan Surat Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi, Nomor: 158/E/KPT/2021.

E-ISSN: 2615-1340 P-ISSN: 2620-7567 Organized by Program Studi Magister Teknik Sipil, Fakultas Teknik.

Published by Universitas Syiah Kuala.
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 Creative Commons License

The Jurnal Arsip Rekayasa Sipil dan Perencanaan (Journal of Archive in Civil Engineering and Planning).
Published by Universitas Syiah Kuala is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on work at http://www.jurnal.usk.ac.id/JARSP/index

 

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