AI-Driven railway regulator inspection planning system: enhancing railway safety inspection prioritisation and incident management

This paper presents an innovative approach, with an application named AI-Driven Railway Regulator Inspection Planning System (the System), to enhance the safety inspection prioritisation and incident management of railways from regulator's perspective. The conventional approach to inspection planning relies on predetermined schedules and the manual selection of inspection items, which is time consuming and not conducive to objectively identifying high-risk assets and optimising resources. The system utilises Natural Language Processing (NLP) and Convolutional Neural Network (CNN) algorithms to quickly extract the semantic meaning of incident and maintenance deferral details, analyse information from various data sources, and derive the relationship among the incidents, maintenance deferrals, and inspection items. Finally, the System assigns weights based on the severity level to generate the normalised risk score of assets. The System also collects and analyses incident data to identify patterns and facilitate trend analysis, with a user-friendly search interface for the efficient retrieval of past cases, assisting in incident analysis. The System was successfully deployed in Electrical and Mechanical Services Department (EMSD), the Government of the Hong Kong Special Administrative Region (HKSAR) of the People’s Republic of China, demonstrating benefits such as an enhanced understanding of incident relationship, streamlined inspection planning, and proactive safety improvement. The System offers valuable guidance for wider application in railway systems, fostering improved safety, operational efficiency, and resource optimisation.