A train detection system is the basis of signaling and control in railway transportation, and there is a trend to apply multiple train detection systems simultaneously to improve reliability and safety. The ideal evaluation process for train detection systems should be able to determine their reliability and safety by considering the track layout, detection block, design and logic of the detection system, routing and frequency of trains, and train length (in relation to block length). Existing evaluation methods for train detection systems still require a few manual interactions and human judgment, which could be inefficient and subjective. Consequently, this research aims to develop a framework with automatic processes to generate all failure scenarios, calculate the probability of fail-safe conditions and wrong-side failures, and finally determine the reliability and safety index of the corresponding connection logic. Results of case studies demonstrate the applicability of the proposed framework to actual cases. The use of this framework can assist railways in identifying the appropriate design and logic of multiple train detection systems.
Risk assessment is an important process for railway safety. Current practices for assessing the risks of driving behaviors aim to inspect the driving record generated by automatic train protection systems. This paper proposes an automatic process to access detailed data contained in driving data, and identifies six high-risk driving behaviors. The modules can assess the competency of drivers and evaluate the frequency of high-risk behaviors in each section. Moreover, an integrated risk index for driving behaviors is proposed to compare each driver and section. An empirical study for drivers and sections is performed to demonstrate the feasibility of applying the proposed modules in practice. Results reveal that 20% of high-risk drivers contribute to 74% of the total risk, while 15% of high-risk sections contribute to 80% of the total risk. The proposed modules identify the drivers and sections with high risk. By enabling the operators of railway systems to take countermeasures, this methodology could enable them to improve the safety of railway systems more efficiently.
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