Factor Analysis of the Sydney Metro's ITS: System Integration Iteration

2. Literature Review

Traditionally, ITS utilizes computational technologies to further advance transport related knowledge towards more capable and increased system performance (Thekdi and Lambert, 2012; Gharehbaghi et al., 2020b). Such enhancements could include the evolution of in-vehicle electronics like multi-tasks computer processes to perform driver support activities. These computational technologies include artificial intelligence and holistically integrated computing (Ojha et al., 2017; Gharehbaghi and Farnes, 2018). The holistic integration of artificial intelligence as a part of complex rail transportation ITS and the verification processes such as those depicted in Figure 1 facilitate the smooth integration of artificial intelligence into the computing platforms.

Figure 1.

Overview of ITS verification process for rail transportation

As it can be observed from Figure 1, for the rail transportations and their systems, the launch of the ITS verification process is the implementation of the system's parameters to establish the principal boundary of the verification system. Following on, the ITS elements and subsequent performance indicators are established to validate its progress. Furthermore, the key functionality requirements are then determined followed by the automation stage where specific certifications are determined. Once the automation stage has been successfully finalized, the simulation stage is commenced to replicate the successful operation of ITS. The final stage of the ITS verification process is the computational modeling to reproduce the proposed ITS, before it is implemented. In addition, the ITS verification process is logical and coherent and thus following it step-by-step will ultimately simplify its execution (Gharehbaghi et al., 2020a). Nevertheless, to fully comprehend the robustness of the ITS, the transportation deficiencies first need to be discussed.