Use of artificial intelligence in maintenance planning and control of public transport – a systematic literature review

Purpose The maintenance of public transport systems, for example trains, buses and airplanes, plays a crucial role in ensuring their availability, safety and cost-effectiveness in their respective means of transport. Effective maintenance planning and control (MPC) not only reduce operational disruptions in these public transport systems but also optimize resource allocation and minimize overall costs. However, with the increasing complexity of today’s public transport networks, there is a growing need for advanced, data-driven maintenance approaches. In this regard, artificial intelligence (AI) has emerged as powerful technology in MPC. AI leverages historical and real-time data to predict potential failures, optimize maintenance schedules and improve decision-making processes. Design/methodology/approach This study systematically reviews and examines existing research on AI approaches for MPC across public transport sectors. The preferred reporting items for systematic literature reviews and meta-analysis (PRISMA) guidelines are employed to ensure a transparent and structured screening process. Descriptive analysis are performed to present emerging research trends, while detailed content analysis synthesizes the findings of the most relevant articles in the research domain. Findings The analysis results show that most of the research focuses on the specific aspects such as predicting the remaining useful life and faults diagnoses, in isolation. However, in practice, MPC involves a complex coordination of many activities, for example inspection, resource allocation and workforce and tasks scheduling. Therefore, the authors believe that there is still need for more holistic solutions that can integrate maintenance activities and shopfloor constraints, moving beyond single-component predictions to system-level maintenance optimization that could be validated in real maintenance facilities. Originality/value The original contribution of this work is to provide the state of the art of AI in MPC for public transport.