Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia
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DOI: 10.1080/00207543.2016.1227102
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- 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.
- 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.
- Christoph Martius & Lutz Kretschmann & Miriam Zacharias & Carlos Jahn & Ole John, 2022. "Forecasting worldwide empty container availability with machine learning techniques," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-24, December.
- M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
- 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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