Demand Forecast of Railway Transportation Logistics Supply Chain Based on Machine Learning Model

Introduction

The continuous development of machine learning has brought new vitality to all walks of life. In recent years, new technologies, such as the Internet and e-commerce, have been widely used with the rapid development of China’s economy. In such a large environment, the industrial structure of Chinese enterprises has been continuously optimized, and the competition among various modes of transportation has become increasingly fierce. Consumers have new development needs for timeliness, speed, convenience, information transparency, differentiation, and many other aspects of logistics. Railway transportation is the key to social and economic development. The development of the railway transportation industry must be given attention, and the adjustment of the scale and structure of the railway should meet the needs of sustainable development. The widespread use of the Internet has led to the rapid development of e-commerce and network economy, and online shopping has been widely accepted as a new way of shopping. Online shopping uses real money, not conceptual money, virtual money, or metal money. Given the large amount of repeated information on the Internet, people cannot easily obtain comprehensive information. Nevertheless, machine learning models can obtain considerable information in the shortest time. Improving the efficiency of obtaining and utilizing information on demand data of railway transportation supply chain has great practical value. This approach is an important guarantee for sustainable and healthy development and the survival and competitiveness of China’s railway freight transportation industry. Therefore, this study has certain research value. In particular, the application of machine learning to the demand forecasting of railway transportation is crucial for optimizing resource allocation and improving the efficiency of railway logistics transportation.

As the foundation of the national economy and social development, railways have been studied by many researchers. Li (2017) took the railway intermodal service supply chain as the research object. They concluded that the railway is still better than other modes of transportation. Gogrichiani and Lyashenko (2021) believed that the important role of railway transport logistics remains. They suggested that a method should be developed to rationalize the criteria for railway line selection. Jayakrishnan, Mohamad, and Yusof (2020) concluded that digitalization brought challenges to the establishment, maintenance, security, and reliability of the Railway Supply Chain (RSC), and the research of machine learning model considerably impacts the demand for railway transportation logistics supply chain. Lustig (2019) believed that research on railway transportation is essential because most railways outside North America are still state-owned. Many scholars have conducted comprehensive and in-depth research on railway transportation. They have comprehensively investigated the economic benefits of railway transportation and the demand for a logistics supply chain. However, they paid considerable attention to the cost of railway transportation but did not consider the demand for logistics supply. However, they neglected the need for logistical supply compared to the cost of rail transport.

A machine learning model is an expression of an algorithm that combs through massive amounts of data to find patterns or make predictions. Many people apply it to research in other fields. Gang (2017) studied the problem of railway logistics demand with machine learning model construction and data mining algorithm. Shruthishree (2021) developed a deep mixed feature machine learning model named AlexResNet+ for the railway logistics demand problem. Sudarmaji (2021) applied machine learning to the demand forecasting process of the railway logistics supply chain to develop a scoring model. Madar (2021) used machine learning to classify and analyze factors from various aspects, such as the economy and railway networks. Machine learning models are used to study demand forecasting in railway transportation logistics supply chains. The research in the field of machine learning has developed to a certain extent. This field can be improved by researchers in many ways. However, machine learning in the logistics supply chain still needs further research.