Using artificial neural networks to predict container flows between the major ports of Asia
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DOI: 10.1080/00207543.2015.1112046
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- Jones, Dean A. & Farkas, Julie L. & Bernstein, Orr & Davis, Chad E. & Turk, Adam & Turnquist, Mark A. & Nozick, Linda K. & Levine, Brian & Rawls, Carmen G. & Ostrowski, Scott D. & Sawaya, William, 2011. "U.S. import/export container flow modeling and disruption analysis," Research in Transportation Economics, Elsevier, vol. 32(1), pages 3-14.
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Cited by:
- 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.
- Yi Xiao & Minghu Xie & Yi Hu & Ming Yi, 2023. "Effective multi‐step ahead container throughput forecasting under the complex context," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1823-1843, November.
- 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.
- Dohee Kim & Eunju Lee & Imam Mustafa Kamal & Hyerim Bae, 2025. "Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 153-172, January.
- Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
- 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.
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