Data for: An Expert System Gap Analysis and Empirical Triangulation of Individual Differences, Interventions, and Information Technology Applications in Alertness of Railroad Workers

Published: 31 March 2020| Version 1 | DOI: 10.17632/8pd69mpd3f.1
James Rodger


In this abstract we would like to provide some exciting concrete information including the article’s main impact and significance on expert and intelligent systems. The main impact is that the PTC expert intelligent system fills in the gaps between the human and software decision making processes. This gap analysis is analyzed via empirical triangulation of rail worker data collected from its groups, individuals and the rail industry itself. We utilize an expert intelligent system PTC information technology application to both measure and to improve the alertness of the groups and workers in order to improve the overall safety of the railways through reduced human errors and failures to prevent accidents. Many individual differences in alertness among military, railroad, and other industry workers stem from a lack of sufficient sleep. This continues to be a concern in the railroad industry, even with the implementation of positive train control (PTC) expert system technology. Information technology aids such as PTC cannot prevent all accidents, and errors and failures with PTC may occur. Furthermore, drug interventions are a short-term solution for improving alertness. This study investigated the effect of sleep deprivation on the alertness of railroad signalmen at work, individual differences in alertness, and the information technology available to improve alertness. We investigated various information and communication technology control systems that can be used to maintain operational safety in the railroad industry in the face of incompatible circadian rhythms due to irregular hours, weekend work, and night operations. To fully explain individual differences after the adoption of technology, our approach posits the necessary parameters that one must consider for reason-oriented action, sequential updating, feedback, and technology acceptance in a unified model. This triangulation can help manage workers by efficiently increasing their productivity and improving their health. In our analysis we used R statistical software and Tableau. To test our theory, we issued an Apple watch to a locomotive engineer. The perceived usefulness, perceived ease of use, and actual use he reported led to an analysis of his sleep patterns that eventually ended in his adoption of a sleep apnea device and an improvement in his alertness and effectiveness. His adoption of the technology also resulted in a decrease in his use of chemical interventions to increase his alertness. Our model shows that the alertness of signalmen can be predicted. Therefore, we recommend that the alertness of all railroad workers be predicted given the safety limitations of PTC.



Artificial Intelligence, Expert System, Sleep Apnea, Employee Stress, Physiology of Sleep