--Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents an online behavior-based monitoring system utilizing a cus-tomised Directed-Graph Neural Network (DGNN) to classify train driver's states into three categories: alert, not alert, and pathological. T o optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accuracy (80.88%) in the three-class model, outperforming models using only facial or skeletal features. Furthermore, this combination achieves over 99% accuracy in the binary alertness classification. Additionally, we introduced a novel dataset that, for the first time, incorporates simulated pathological conditions into train driver monitoring, broadening the scope for assessing risks related to fatigue and health. This work represents a step forward in enhancing railway safety through advanced online monitoring using vision-based technologies. Fatigue is a critical safety concern in railway operations, where long shifts and repetitive activities can significantly impair a driver's alertness [1]. Despite regulations aimed at ensuring adequate rest for train drivers, fatigue-related incidents remain alarmingly common. According to research by the AA Charitable Trust, one in eight drivers admits to falling asleep at the wheel, while nearly two-fifths have felt so tired that they feared they might nod off [2]. The risk is particularly high with modern automated trains operating on night shifts and monotonous routes [3].

Defining the roadmaps for Artificial Intelligence applications for railway operations and network management Applications are invited for a PhD studentship in innovative approaches in artificial intelligence for railway scheduling and operations, to be based in Institute for Transport Studies at University of Leeds. The position is an opportunity to combine cutting-edge research at the intersection of railway scheduling and artificial intelligence techniques such as machine learning, neural networks. The overall objective of the PhD research project is to investigate the potential of Artificial Intelligence (AI) in the rail sector and contribute to the definition of roadmaps for future research in operational intelligence and network management. In particular, the student will develop and compare different AI approaches, e.g. machine learning, deep and reinforcement learning, for railway traffic planning and management. He or she will have a chance to investigate using AI for solving combinatorial optimization problems, AI for supporting optimization models, with special focus on the optimization models for railway operations and management.