Railway Freight Volume Forecast Based on GRA-BP Model

DOI

10.2991/978-94-6463-570-6_99How to use a DOI?

Keywords

railway freight volume; prediction; grey relational analysis; BP neural network

Abstract

In order to improve the accuracy of railway freight volume prediction, we adopted the BP neural network method combined with grey relational analysis (GRA). Given that BP neural network algorithm are prone to local minimum, slow learning convergence, and diversity of structural selection problems, we especially introduce GRA to optimize the prediction process. In the construction of GRA-BP prediction model, based on the statistical yearbook data, we first selected the key indicators such as railway freight turnover rate, raw coal output, railway operating mileage, highway and waterway freight volume and the added value of the primary industry as the main factors affecting the railway freight volume. These indicator data were then used as input for the GRA-BP model, where the first 70% of the data were used for model training and the last 30% for testing. After training and testing, we obtained the prediction results of railway freight volume, and calculated the evaluation indexes such as MSE, RMSE, MAE, MAPE and R². The results showed that the GRA-BP prediction model performed well in the nonlinear fitting, and the prediction accuracy achieved the expected effect.

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  - Wenyan Wang
AU  - Yanbin Wang
PY  - 2024
DA  - 2024/11/22
TI  - Railway Freight Volume Forecast Based on GRA-BP Model
BT  - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
PB  - Atlantis Press
SP  - 990
EP  - 998
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-570-6_99
DO  - 10.2991/978-94-6463-570-6_99
ID  - Wang2024
ER  -