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In recent years, with the gradual networking of high-speed railways in China, the existing railway transportation capacity has been released. In order to improve transportation capacity, railway freight transportation enterprises companies have gradually shifted the transportation of goods from dedicated freight lines to passenger-cargo lines. In terms of the organization form of collection and distribution, China has a complete research system for heavy-haul railway collection and distribution, but the research on the integration of collection and distribution of the ordinary-speed railway freight has not been completed. This paper combines the theories of the integration of collection and distribution theory, coordination theory, and coupling theory and incorporates the machine learning fuzzy mathematics to construct an “Entropy-TOPSIS Coupling Development Degree Model” for dynamic intelligent quantitative analysis of the synergy of railway freight collection and distribution systems. Finally, we take the Tongchuan Depot of “China Railway Xi’an Group Co., Ltd.” as a research object to construct a target system and use the intelligent information acquisition system to collect basic data. The analysis results show that through the coordinated control of the freight collection and distribution system, the coordination between the subsystems of the integrated freight collection and distribution system is increased by 5.94%, which verifies the feasibility of the model in the quantitative improvement of the integration of collection and distribution system. It provides a new method for the research of integrated development of railway freight collection and distribution.

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The existing schemes show that the application of an Integrated Choice and Latent Variable (ICLV) model in choice behaviors of railway shippers in freight services provided by China Railway Express (CRE) and private logistics enterprises (PLEs) is a challenge. Therefore, this paper focuses on the freight service products of CRE and PLEs and constructs a Fuzzy ICLV model by combining a fuzzy comprehensive evaluation with the ICLV model to analyze choice behaviors in freight services of railway shippers. The Fuzzy ICLV model offered significant advantages because it allowed us to incorporate latent variables that regard perceived convenience and reliability with a traditional discrete choice model (DCM). The results of the Fuzzy ICLV model clearly confirm that shippers' perceptions of the convenience and reliability of freight service products impact their freight service product choices. The results also show that the freight, time, distance from shippers to the sales department, destination distance, occupation, monthly income level, order quantity, cargo weight, and type are all major factors that affect Chinese railway shippers' choice behaviors for freight services. The goodness-of-fit measure of the Fuzzy ICLV model is 0.325, which is higher than that of the Nested Logit (NL) model. The results of this study can fill the gaps in choice behaviors in freight services research on Chinese railway shippers and have important implications for operators of CRE to adjust their freight service strategies or products.