Radiocommunication systems between train and trackside (RSTT) carry essential information for train operations between on-board radio equipment and the related radio infrastructure located along the trackside, such as the train control, voice dispatching, command, operational information as well as the monitoring data. In a high-speed railway environment, the electromagnetic interference (EMI) has been a major threat to RSTT, and may result in critical security issues for railway transportation and even passengers. Given the complex scenario of the high-speed railway, it is significant to monitor the impact of disturbances in order to guarantee the quality of RSTT. On one hand, RSTT operate in a complex electromagnetic environment where transient disturbances coexist with permanent ones, and they both vary dramatically while the train is running. On the other hand, various radiocommunication technologies have been used for railway applications, featuring forward error-correction codes to resist EMI. Therefore, this paper puts forward a novel approach to evaluate the impact on radio transmission based on the joint statistical characteristics of time-varying EMI. This approach applies a dynamic effective signal-to-interference-plus-noise ratio mapping model to establish the relation between the block error performance of on-board radio and the joint statistical characteristics of disturbances with a mutual information-based metric. Simulations on radio transmission using Turbo coding and low-density-parity-check coding under various interferences indicate that this approach is effective to evaluate the degradation of transmission signal with forward error-correction coding due to EMI with different characteristics.

In railway electromagnetic environment, transient disturbances usually degenerate the function of railway wireless communication systems, thus may cause severe security issues. To protect communication services, measurement methodology to characterize disturbances keeps evolving. Nowadays, the statistical characteristics of disturbances, such as Amplitude probability distribution (APD), have been proven more efficient to assess the degradation of digital transmission than Quasipeak detection in most Electromagnetic Compatibility standards. To demonstrate the impact of disturbances on error performance more precisely, a more refined method to estimate Bit error probability (BEP) of digital modulation is necessary. The APD value is ideally obtained with the equivalent bandwidth of the radio service under threat, which means specific apparatus of certain radio service is consequently indispensable. This paper proposes a novel method to estimate the maximum BEP of multilevel amplitude modulations based on APD acquired by standard apparatus. The calculated BEP also provides reference for APD emission restriction.

The construction of multilayer rail transit systems is a necessary way to realize “modern metropolitan areas on rail”, improve resource sharing, and increase travel services, where data integration is of utmost importance. To break data silos and realize data flow between different rail systems, a fine-grained access control framework is proposed in this paper. Through categorical and hierarchical schemes, a universal security scale is established for cross-domain data resources. Based on this, a trust and sensitivity attribute-based access control (TSABAC) model is put forward to describe the characteristics of the access control process. Furthermore, the method of policy integration is discussed, as well as the solution to the policy incompatibility problem, due to cross-domain interaction. As shown in practical application and simulation analysis, this framework can meet the requirements of security and granularity. This research is of great significance for promoting the high-quality development of urban agglomerations and metropolitan areas, and improving the quality and efficiency of rail transit.

Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources.