@article{Zhou_Hu_2019,
	author = {Zhou, Xinyue and Hu, Qinyou},
	title = {Estimation of Shipment Size in Seaborne Iron Ore Trade},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {13},
	number = {4},
	pages = {791-796},
	year = {2019},
	url = {./Article_Estimation_of_Shipment_Size_in_Zhou,52,956.html},
	abstract = {Shipment size is unavailable and important in AIS-based trade volume estimates. A method of shipment size estimates based on AIS (Automatic Identification System) data and BP neural network is proposed. The ship's length, width, designed draught, current draught and deadweight ton are input parameters, the actual shipment size of the ship is output value, and the BP neural network is trained to estimate the actual shipment size of the iron ore carriers. Then, the AIS data is used to calculate the iron ore trade volume in 2018. Compared with customs data, the annual error of import volume of China is less than 0.5%. The result shows that the proposed method is accurate and practical.},
	doi = {10.12716/1001.13.04.11},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Cargo Handling, AIS Data, Shipment Size, Estimation of Shipment Size, Seaborne Iron Ore Trade, Maritime Mobile Service Identify (MMSI), BP Neural Network, Iron Ore}
}