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http://dx.doi.org/10.38121/kpea.2021.09.37.3.1

Forecasting the Daily Container Volumes Using Data Mining with CART Approach  

Ha, Jun-Su (인하대학교 물류전문대학원)
Lim, Chae Hwan (인하대학교 물류전문대학원)
Cho, Kwang-Hee (인하대학교 물류전문대학원)
Ha, Hun-Koo (인하대학교 물류전문대학원)
Publication Information
Journal of Korea Port Economic Association / v.37, no.3, 2021 , pp. 1-17 More about this Journal
Abstract
Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.
Keywords
port volume forecasting; data mining; CART; daily demand forecasting;
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Times Cited By KSCI : 1  (Citation Analysis)
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