Train Driver Visual Performance: A Parametric Survival Model of Reaction Time

Abstract

The implementation of Automatic Train Operation (ATO) systems is rapidly increasing in the railway industry, driven by the potential for improved efficiency, safety, and capacity. However, one of the key challenges in implementing ATO systems is defining the functional requirements of these automated systems. One approach for deriving these functional requirements is by evaluating human performance in the driving task.This study investigates the visual performance of train drivers under different conditions using a driving simulator. 18 active train drivers participated in the experimental study. The task of the participants in the simulator study was to drive a train and respond to stationary visual stimuli in the form of cubes at irregular intervals by pressing the signal horn. The study aimed to assess reaction times to objects under three different train protection systems: PZB (punktförmige Zugbeeinflussung, intermitted train protection system), ETCS (European Train Control System level 2 with cab signalling), and driving-on-sight. The square objects varied in size (90 cm and 180 cm) and color (brown and orange), creating a diverse range of scenarios for the drivers.Survival analysis, a statistical method used to analyze time-to-event data, was employed to evaluate the drivers’ performance. In this context, the ‘event’ was defined as the driver reacting to an object, and the ‘time’ was the reaction time. This method provided a detailed understanding of the distribution of reaction times and how they were influenced by the different driving conditions. The hazard function, which describes the instantaneous rate of occurrence of the event before its actual occurrence, was used to compare reaction times across different conditions. The results of the study revealed that object size, object contrast, and train speed had a significant effect on train drivers’ reaction time. Specifically, larger and more contrasting objects were associated with faster reaction times. Stimuli were detected more quickly at higher speeds, possibly due to the verticalization of gaze at increasing speeds and the objects visually enlarging more rapidly at higher speeds. Interestingly, the differences between train protection systems yielded complex results that warrant further investigation. Additionally, the probabilities that a driver had not yet reacted to an object by a certain time were estimated. It is important to note that although the study was conducted in a sophisticated simulation environment, it does not fully represent the real conditions of driving a train. Nevertheless, the study provides valuable findings into visual perception performance of train drivers. Furthermore, it demonstrates the utility of survival analysis in railway domain, particularly for analyzing reaction time data. The findings of this study have significant implications for the railway industry, particularly in the context of deriving functional requirements of ATO implementations. Understanding how human performance is influenced by operational and environmental factors can inform the design of safer and more efficient railway operations.

Keywords: Automatic Train Operation, Reaction Time, Visual Perception

DOI: 10.54941/ahfe1005255