Assessment and Monitoring of Mental Workload in Subway Train Operations Using Physiological, Subjective, and Performance Measures Publisher



Jafari MJ¹ ; Zaeri F² ; Jafari AH³ ; Payandeh Najafabadi AT⁴ ; Alqaisi S⁵ ; Hassanzadehrangi N⁶ Show Affiliations
Source: Human Factors and Ergonomics In Manufacturing Published:2020

Abstract

Subway train operation is a complex, sociotechnical system that involves a variety of cognitively demanding tasks. The train operators are responsible for continuously monitoring the surrounding environment, maintaining awareness, processing information, and making decisions under risk. The resulting mental strain on operators can negatively affect their performance and the interaction of the human–machine system. The objective of this study was to evaluate if physiological, subjective, and performance measures could identify the level of mental workloads arising from routine and nonroutine operations in the subway system. A total of 11 subway train operators underwent different driving scenarios in a high-fidelity simulator. The simulated tasks were divided into two categories: routine operations (preparing to drive and driving between stations without interruptions or emergencies) and nonroutine operation (responding to a tunnel fire, dealing with a high density of passengers, encountering a passenger/technician on the track, and responding to train failure). The mental workload was monitored and evaluated in these tasks using an electrocardiogram, subjective self-rating scales, and driving performance. Both heart rate variability and performance measures (including reaction time and error rate) detected mental workload variations in the different operations. On the other hand, the subjective ratings (including NASA-TLX) assessed the overall mental workload associated with a task, without explaining the mental demand variations within the task over time. Subway train drivers experienced different levels of mental workload during routine and nonroutine driving conditions. The findings of this study can be used to extract mental workload limits to optimize workload levels during train operations. © 2019 Wiley Periodicals, Inc.