Research on Dairy Railway Cold Chain Transportation Volume Prediction method Based on Neural Network and Competition Sharing Rate Combination Model

Mastering the development trend of railway cold chain logistics volume is a prerequisite for realizing the market-oriented operation of railway cold chain logistics, promoting the transformation of railway cold chain logistics to modern logistics, and formulating railway cold chain transportation organization and operation plans. To reasonably predict the dairy railway cold chain transportation volume, this paper combines the neural network with the modal share model to construct a dairy railway cold chain transportation volume prediction combination model based on neural network and modal share, based on the development status and influencing factors of China's railway cold chain transportation volume. With the data of dairy production and per capita dairy consumption in various provinces from 2012 to 2022, the dairy railway cold chain transportation volume between major sections is predicted. According to the results of the model solution, it can reflect the dairy railway cold chain transportation volume and its changing trend, providing a basis for the decision-making of railway cold chain logistics business development.

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Publication Details

Published
2024-08-23
DOI
https://doi.org/10.1145/3695652.3695664
Primary Topic
Grey System Theory Applications
Type
article
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article

Research on Dairy Railway Cold Chain Transportation Volume Prediction method Based on Neural Network and Competition Sharing Rate Combination Model

Y.X. Yao, Lianmin Zhou, Weiwei Gong, Cheng Chen et al.
Grey System Theory Applications
article

Research on Dairy Railway Cold Chain Transportation Volume Prediction method Based on Neural Network and Competition Sharing Rate Combination Model

Y.X. Yao, Lianmin Zhou, Weiwei Gong, Cheng Chen, Yan Wang
article en

Abstract

Mastering the development trend of railway cold chain logistics volume is a prerequisite for realizing the market-oriented operation of railway cold chain logistics, promoting the transformation of railway cold chain logistics to modern logistics, and formulating railway cold chain transportation organization and operation plans. To reasonably predict the dairy railway cold chain transportation volume, this paper combines the neural network with the modal share model to construct a dairy railway cold chain transportation volume prediction combination model based on neural network and modal share, based on the development status and influencing factors of China's railway cold chain transportation volume. With the data of dairy production and per capita dairy consumption in various provinces from 2012 to 2022, the dairy railway cold chain transportation volume between major sections is predicted. According to the results of the model solution, it can reflect the dairy railway cold chain transportation volume and its changing trend, providing a basis for the decision-making of railway cold chain logistics business development.

China Academy of Railway Sciences (CN)
Openalex Percentile: Top 78%
Grey System Theory Applications
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