Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations

The rapid evolution of railway systems, driven by digitization and the proliferation of Internet-of-Things (IoT) devices, has resulted in an unprecedented volume of diverse and complex data. This railway big data offers immense opportunities for advancing safety, efficiency, and sustainability in transportation but presents significant analytical challenges due to its heterogeneity, high-dimensionality, and temporal dependencies. Existing approaches often fall short of fully exploiting these data characteristics, struggling with multi-source integration, real-time predictive capabilities, and adaptability to dynamic environments. To address these gaps, we propose a novel framework leveraging deep learning techniques tailored to railway big data. Our method integrates temporal encoders and spatial graph neural networks, combined with domain-specific knowledge and contextual awareness, to achieve robust anomaly detection, predictive maintenance, and passenger demand forecasting. By capturing both spatial relationships and temporal patterns, the proposed framework ensures comprehensive insights into system behavior, enabling proactive decision-making and operational optimization. Experimental results on real-world railway datasets demonstrate superior performance in accuracy, scalability, and interpretability compared to traditional methods, underscoring the potential of our approach for next-generation intelligent railway systems. This work aligns with the goals of integrating big data and AI for environmental and operational improvements in railway transportation, contributing to a sustainable, resilient, and adaptive infrastructure capable of meeting future mobility demands.