The rail transit system plays a crucial role in modern transportation. With the increasing demand for clean and green energy in the transport sector, its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail transit. However, the gradual development of the rail transport energy system has led to an increase in its complexity, and the rising difficulty of system assessment has faced the limitations of traditional assessment methods. Hence, it is essential to develop effective assessment methods. This paper begins by providing a systematic review of the development status of Reliability, Availability, Maintainability and Safety (RAMS) assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy systems. Subsequently, based on the four fundamental properties of RAMS, it summarizes the current state of key assessment technologies in the field of rail transit. Moreover, the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy systems. Finally, the paper offers an outlook on the future development of RAMS assessment for rail transport energy systems. By comprehensively analyzing these aspects, the paper aims to contribute valuable insights into optimizing the rail transit energy system, promoting its sustainable and efficient operation in the context of clean and green energy utilization.

Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally, but also predict the train’s future operating status. How to obtain valuable information from massive vehicle data is a difficult point. First, we divide the vehicle data of a high-speed train into 13 subsystem datasets, according to the functions of the collection components. Then, according to the gray theory and the Granger causality test, we propose the Gray-Granger Causality (GGC) model, which can construct a vehicle information network on the basis of the correlation between the collection components. By using the complex network theory to mine vehicle information and its subsystem networks, we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network. In addition, the vehicle information network is weak against attacks, but the subsystem network is closely connected and strong against attacks.