Sydney Metro and Melbourne Metro Rail Stochastic Comparison and Review: Scrutiny of AI

This paper aims to review an innovative artificial intelligence (AI) apparatus to enhance the rail transportation performance. In this light, the Sydney Metro and Melbourne Metro rail will be compared, since both of these Australian rail networks employ complex AI as part of their overall performance enhancement schemes. These two case studies further highlight the novel critical aspects of AI in rail transportation sector such as recalibration through smart system design and automation, nonlinear controls and precise design, modeling and control apparatus, and so on. As a part of such a view, different aspects of AI systems such as increased reliability and safety were also investigated. This research found that with such enhancements of system performance, the overall transportation functioning would ultimately be significantly improved. Subsequently, AI in the Australian context can be further refined based on comprehensive integration of the key factors.