A kNN Based Voyage's Containers' Entering Time Distribution Prediction System

Compared with the air transportation and land transportation, water transportation has many advantages such as larger loading capacity, lower unit transportation cost, lower construction investment and so on. What's more, water transportation has played an important role in the economical devel...

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Bibliographic Details
Published in2021 IEEE International Conference on Progress in Informatics and Computing (PIC) pp. 484 - 488
Main Authors Shen, Shitong, Cao, Jian, Yang, Yinyue, Guo, Yameng
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.12.2021
Subjects
container entering time prediction
container terminal operation
Containers
Costs
data mining
International trade
Land transportation
Loading
machine learning
Machine learning algorithms
Statistical analysis
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Summary:Compared with the air transportation and land transportation, water transportation has many advantages such as larger loading capacity, lower unit transportation cost, lower construction investment and so on. What's more, water transportation has played an important role in the economical development of China, especially in the aspect of international trade. Therefore, the improvement in the efficiency of water transportation will be of great significance. In this paper, we designed a system to predict the containers' entering time distribution of a given voyage at a specific port by using machine learning algorithms and statistical methods. Using Shanghai Yangshan Port phase IV automated terminal's data, we perform some experiments, and the result shows that our system can provide valid predictions.
ISSN:2329-6259
DOI:10.1109/PIC53636.2021.9687057