Artificial intelligence applications to enhance risk management of railway station operations

Alawad, Hamad Ali ORCID: 0000-0001-9871-1588 (2022). Artificial intelligence applications to enhance risk management of railway station operations. University of Birmingham. Ph.D.

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Abstract

Railway stations are a vital part of the railway system and a critical hub of public transportation. This valuable infrastructure in the heart of cities requires effective risk management processes as well as high-level safety. However, railway stations have faced increasing operational challenges in recent years as a result of the growth in urban areas, high demand, intensive usage, and an increase in train capacity often without any consideration of the size of the existing stations. Consequently, increased risks may exist and lead to train delay, injuries, or fatalities as well as loss with disruption of the whole system and impact on business. Risk management is thus an essential part of the safety system, a system which currently is mostly traditional and does not take advantage of available technologies. The traditional risk process has uncertainties, it takes time, is costly and it depends on experts who are not available all the time. These processes could be developed to cope with future requirements by creating smarter stations and cities, embracing new technologies such as artificial intelligence (AI). It is both wise and necessary to make use of all possible technologies to stop or mitigate the risks and consequences. This can be achieved by improving the risk management process, which will then reflect positively on the quality, reliability, safety, security, as well as cost. This study proposes utilising (AI) methods to improve railway stations' safety and risk management. The methods have powerful analytics and predictive ability. Machine learning (ML) has been proposed using the classification tree, Fuzzy and Nerul network (ANFIS), and deep learning (DL) to identify the risks and people’s behaviours in the stations. The information examined is from occurrences at UK stations and ended in casualties. The characteristics of the process and the selected approach are fundamental to selecting the appropriate data. An advanced analytical approach is adopted, using predictions via classification in addition to timely detections. The proposed application presents novel work contributing to the field and aiming to save lives in the future. DL shows a great opportunity to capture many risks in the stations. The outcomes provide a unique contribution where the approach is applicable and makes the process smarter with less human intervention, whilst also supporting decision-makers in real-time. This will also upgrade the risk management to be effective, reflect positively on people’s lives and provide a safe environment for all of society.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):