The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network
The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the stor...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/5764602 |
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| _version_ | 1832559524338204672 |
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| author | Yinping Gao Daofang Chang Ting Fang Yiqun Fan |
| author_facet | Yinping Gao Daofang Chang Ting Fang Yiqun Fan |
| author_sort | Yinping Gao |
| collection | DOAJ |
| description | The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers. |
| format | Article |
| id | doaj-art-e64afa49189f417ca954115bec7b4986 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e64afa49189f417ca954115bec7b49862025-02-03T01:29:58ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/57646025764602The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural NetworkYinping Gao0Daofang Chang1Ting Fang2Yiqun Fan3Institute of Logistics Science and Engineering, Shanghai Maritime University, 201306 Shanghai, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, 201306 Shanghai, ChinaSchool of Economics and Management, Shanghai Maritime University, 201306 Shanghai, ChinaShanghai Municipal Engineering Design Institute (Group) Co., Ltd., 200092 Shanghai, ChinaThe effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.http://dx.doi.org/10.1155/2019/5764602 |
| spellingShingle | Yinping Gao Daofang Chang Ting Fang Yiqun Fan The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network Journal of Advanced Transportation |
| title | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network |
| title_full | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network |
| title_fullStr | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network |
| title_full_unstemmed | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network |
| title_short | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network |
| title_sort | daily container volumes prediction of storage yard in port with long short term memory recurrent neural network |
| url | http://dx.doi.org/10.1155/2019/5764602 |
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