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Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia

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  • Gu Pang
  • Bartosz Gebka

Abstract

We propose a new approach to forecasting total port container throughput: to generate forecasts based on each of the port’s terminals and aggregate them into the total throughput forecast. We forecast the demand for total container throughput at the Indonesia’s largest seaport Tanjung Priok Port, employing SARIMA, the additive and multiplicative Seasonal Holt-Winters (MSHW) and the Vector Error Correction Model (VECM) on the monthly port and individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on mean absolute error and root mean squared error. Our results show that the MSHW model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The VECM provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port’s capacity and construction of new container terminals at Tanjung Priok Port.

Suggested Citation

  • Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:9:p:2454-2469
    DOI: 10.1080/00207543.2016.1227102
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    Cited by:

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    2. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
    3. Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
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    6. Feng, Hongxiang & Grifoll, Manel & Zheng, Pengjun, 2019. "From a feeder port to a hub port: The evolution pathways, dynamics and perspectives of Ningbo-Zhoushan port (China)," Transport Policy, Elsevier, vol. 76(C), pages 21-35.

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