Considering spatial correlation in shipping network is an effective method to improve veracity of the short-term freight volume prediction. But the shipping network is much more complicated than the highway transportation. The high-dimensional data of shipping network cannot be used effectively. A short-term freight volume prediction model FR-NN which is based on frequent pattern mining and neural network is constructed. Frequent ports in cargo network are found out according to idea of frequent pattern mining, and they represent the main spatial relations in network. Neural network is used for fitting statistical relationships among ports. Example analysis shows that the model can improve veracity of the short-term freight volume prediction in different time particle sizes. Furthermore, the model can forecast to the weeks and days freight volume, that the time series method is not ideal for the prediction results.
CHEN Chen，WU Qing，GAO Song
. Short-Term Shipping Freight Volume Prediction Based on
Temporal-Spatial Features[J]. Journal of Shanghai Jiaotong University, 2019
, 53(5)
: 556
-562
.
DOI: 10.16183/j.cnki.jsjtu.2019.05.007
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