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

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

Koorosh Gharehbaghi, Kathryn M. Robson, Neville Hurst, Matt Myers
Copyright: © 2021 |Pages: 11
DOI: 10.4018/IJoSE.2021070103
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Abstract

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.
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2. Literature Review

Gharehbaghi and McManus (2018b) noted that, while AI is the capacity of machines, computers and systems to independently think, learn and operate. Loannou, (2008) also highlighted their elements however range from complex reasoning problem-solving to intricate independent operations. On the other hand, Gharehbaghi and Farnes (2018) distinguished that generally there are four broad AI types, including limited memory, reactive machines, self-awareness and finally theory of mind. Moreover, Chmielowski (2016) underlined that, the traditional problems (or goals) of AI range from natural language processing to manipulate objects. Globally however, AI techniques consist of Artificial Neural Networks (ANN) to Fuzzy-Markov computation among many other methods (Gharehbaghi, 2016b; Bachmann et al., 2013, Kim, 2011).

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