Deep Learning Based Method for Early Warning of Price Risk in Railroad Bulk Freight Transport with Consideration of Time Series of Comprehensive Impact Index

Abstract

The aim in this study is to enhance the price competitiveness of medium- and long-haul railroad bulk cargoes in the transportation market. Efficiently triggering an early warning for railroad bulk freight price risk is crucial. This paper discusses relevant factors affecting the pricing of railway freight from four aspects, namely the macroeconomy, the transportation market, the freight owner, and the railway enterprise. We construct a comprehensive index system for generating an early warning of railroad bulk freight prices. Utilizing a collected data set of 20 factor indicators that affected prices from 2015 to 2017, we calculate a comprehensive price risk warning index using the integrated entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. We propose a risk warning level classification method based on the k-means clustering method to determine five levels of price risk warning. Subsequently, we establish comprehensive index prediction methods for issuing price risk warnings based on the back propagation (BP) neural network and the deep learning long short-term memory (LSTM) network. We verify that the prediction performance of the deep learning LSTM network is significantly better than that of the BP neural network by combining all coal freight data from the Shanghai Railway Bureau for the period 2015 to 2017 as a case study. The proposed method can predict the level of freight price risk more accurately in the next phase, based on the status of the factor indicators, and be used to assist railway departments in developing a more reasonable freight price adjustment strategy to scientifically mitigate freight price risk.