Abstract
Railway freight volume is an important part of the total social freight volume and an important indicator of the national economy. Scientific prediction of railway freight volume can provide decision support for the formulation of China's railway policy and railway investment planning, and is of great significance for adjusting transportation structure and building an efficient transportation network. In order to improve the prediction accuracy, this paper constructs a combined prediction model based on GRA-GABP. The model uses grey correlation analysis to screen out the key influencing factors of railway freight volume, and optimizes the weight and threshold of BP neural network based on genetic algorithm to improve the prediction accuracy. This paper comprehensively considers the influencing factors of macroeconomics, market demand, logistics competition and railway supply. The historical data of railway freight transport from 1978 to 2018 is selected for case analysis. The results show that the prediction accuracy of the GRA-GA-BP based combination prediction model is significantly improved and can be used as an effective tool for railway freight volume forecasting.
Highlights
Railway freight plays an important role in the transportation of bulk cargo such as coal and grain, and is an important force in promoting the development of the national economy
Zhang et al used grey correlation analysis and support vector machine based on Gaussian kernel function to establish a railway freight volume prediction model [3]
The changes in railway freight volume are affected by many factors, including macroeconomics, market demand, logistics competition, and railway supply
Summary
Railway freight plays an important role in the transportation of bulk cargo such as coal and grain, and is an important force in promoting the development of the national economy. In 2018, the General Office of the State Council issued the "Three-Year Action Plan for Advancing the Adjustment of Transport Structure", stating that by 2020, the volume of bulk cargo transported by the railway should be significantly improved. Under such a background, it is important to scientifically predict the amount of rail freight. Shao et al combined the grey prediction model with the BP neural network model to construct a combined model of grey-neural networks [6] These studies have problems with small data samples and large prediction errors, and BP neural networks have the drawback of being trapped in local minimums. This paper builds a combined prediction model based on GRA-GA-BP to improve prediction accuracy
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