The Impact of Artificial Intelligence on Passenger Flow in Air and Rail Integrated Networks: A Systematic Literature Review

Abstract

Integrating air and rail transportation systems offers an opportunity to enhance global mobility, minimize environmental impacts, and improve operational efficiency. This study explores the role of Artificial Intelligence in addressing passenger behaviour and interoperability in air-rail networks. Advanced AI techniques, including deep learning, reinforcement learning, and predictive modelling, are employed to analyse passenger behaviour patterns and optimize multimodal integration. The findings highlight the importance of tailored solutions for different passenger groups, emphasizing that a one-size-fits-all strategy is inadequate. Economic and environmental evaluations underline the broader social benefits of integration, such as reduced travel times, increased productivity, and lower emissions. Methodologically, this paper uses a systematic literature review to synthesize insights and identify trends. Ethical and technical challenges, including data integration, algorithmic bias, and privacy concerns, are addressed, underscoring the need for strong governance mechanisms. The study advocates for advancing predictive maintenance, developing AI-driven personalized options, and establishing global standards for multimodal transportation. By fostering cross-sector partnerships, the research contributes a comprehensive framework to enhance air-rail integration using cutting-edge AI technologies, promoting sustainable and intelligent transport systems.