Research on Freight Rate Prediction of China-Europe Route Based on Bilstm Model and AIS Data

DOI

10.2991/978-94-6463-552-2_3How to use a DOI?

Keywords

component; Shipping; Freight Rate Prediction; AIS; Bilstm

Abstract

In the context of globalization, the shipping industry plays a crucial role in international trade, and fluctuations in freight rates have significant impacts on the global economy. This study integrates Automatic Identification System data with shipping schedule data to calculate the daily total capacity and average capacity of ships, which serve as key factors influencing freight rate predictions. By incorporating real-time AIS data, the model captures dynamic ship movements and provides more accurate capacity estimates. A Bidirectional Long Short-Term Memory model is employed and compared with Long Short-Term Memory and Recurrent Neural Network models. Additionally, hyperparameter optimization methods including Tree-structured Parzen Estimator, Bayesian Optimization, Random Search, and Grid Search are applied and compared. The results indicate that the Bilstm model with AIS data outperforms the other models in terms of Mean Absolute Error, Mean Absolute Percentage Error, and Coefficient of Determination. Among the optimization methods, the TPE method demonstrates superior performance, providing the most accurate and reliable freight rate predictions. This study highlights the importance of integrating real-time AIS data and advanced optimization techniques in improving the accuracy of freight rate prediction models.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhiping Li
AU  - Ruihang Cui
AU  - Jieshu Zhang
AU  - Kai Xu
AU  - Cuiyun Zhao
PY  - 2024
DA  - 2024/10/27
TI  - Research on Freight Rate Prediction of China-Europe Route Based on Bilstm Model and AIS Data
BT  - Proceedings of the 4th International Conference on Management Science and Software Engineering (ICMSSE 2024)
PB  - Atlantis Press
SP  - 12
EP  - 26
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-552-2_3
DO  - 10.2991/978-94-6463-552-2_3
ID  - Li2024
ER  -