Utilizing Big Data for Enhancing Passenger Safety in Railway Stations

Abstract

In light of the increasing demand and capacity in the railway industry, it is imperative to maintain safety in relation to the complexities of the substantial railway stations. Thus, it is important to take note of the time where investments in new technologies directed at the safety of the railway enable safety and protection in this area. Novel technological techniques such as big data analysis (BDA), data mining or machine learning (ML) have been developed and applied in many areas such as sales, banking and healthcare. The development of such methods has important benefits within the context of railway safety, however, these new methods need to be implemented and developed with consideration of whether these operational models can help to solve the various difficulties that currently exist in the risk analysis of railway stations. Moreover, as the adoption of the Internet of thing (IoT) grows, it is expected that analytical needs for handling data will also increase. It has been shown that the progression towards automation and applying such innovative new technologies such as BDA may be a powerful tool for integration in the future of transportation in general and the railway industry in particular, whereby analytical predictions can aid in the development of safer railway stations which have greater potential for ensuring the safety of passengers. In this paper a Bow Tie (BT) framework model has been created to combine BDA into the risk assessment process. The BDA can be beneficial to the risk assessment, support the decision makers in real time, and reduce human errors. This method can be fully integrated into passenger data and the business model for the railway station. Employing the existing safety records utilizing BDA is expected to mitigate risks, predict hazards, raise safety and security efficiency and reduce the cost.