Abstract The shift toward sustainable railroad energy systems calls for intelligent, human-centered solutions that consider not only technical performance but also behavioral dynamics and social impact. This study proposes a behavioral analysis framework powered by machine learning to assess human-technology interaction patterns within the evolving landscape of railway energy innovation. Leveraging a multi-stage approach—combining text mining, topic modeling, network analysis, and empirical triangulation—we analyze 1234 patents filed between 2000 and 2023 across the USPTO, EPO, and WIPO. Through TF-IDF, human-centered LDA modeling, and centrality metrics, we identify innovation clusters emphasizing intuitive user interfaces, adaptive control, and participatory design. Case studies of Deutsche Bahn, BNSF Railway, and Singapore MRT, along with longitudinal analyses of user behavior, highlight the importance of aligning technological sophistication with user engagement. Results suggest that human-centered technologies achieve adoption rates up to 34% higher than purely technical alternatives. Furthermore, a Long Short-Term Memory (LSTM)-based prediction model indicates that by 2030, such technologies could represent approximately 65% of new railroad energy investments. This research provides a rigorous framework for technology assessment and policy development, advancing the design of intelligent railroad systems that are not only efficient, but socially responsive and behaviorally informed.
Yong-Jae Lee (Fri,) studied this question.
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