Analysis of configuration data errors in Communication-based Train Control systems

Communication-based Train Control (CBTC) systems combining intelligent instruments and wireless communications are increasingly used in modern metros and other railway systems, because they can help optimizing railway traffic management and minimizing headways between running trains. Despite the potential benefits, CBTC systems functionality and availability can be affected by their configuration data errors, induced by human errors, instruments physical degradations and cyber communication interventions. In this paper, we identify and classify configuration data errors in CBTC systems, and propose a Monte Carlo (MC) simulation-based exploration approach, with safety margins estimation, for the primary purpose of verifying and assessing the data error effects on system functionality. The approach is illustrated with reference to a CBTC model prototyped from a Beijing Mass Transit Railway (MTR) operation system. Potentially risk-significant data errors of random magnitudes and running positions are injected by a Monte Carlo-driven engine and the system functional response is assessed.