Machine learning noise exposure detection of rail transit drivers using heart rate variability

Previous studies have found that drivers’ physiological conditions can deteriorate under noise conditions, which poses a potential hazard when driving. As a result, it is crucial to identify the status of drivers when exposed to different noises. However, such explorations are rarely discussed with short-term physiological indicators, especially for rail transit drivers. In this study, an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers’ physiological responses. Considering the individuals’ heterogeneity, this study introduced drivers’ noise annoyance to measure their self-noise-adaption. The variances of drivers’ heart rate variability (HRV) along with different noise adaptions are explored when exposed to different noise conditions. Several machine learning approaches (support vector machine, K-nearest neighbour and random forest) were then used to classify their physiological status under different noise conditions according to the HRV and drivers’ self-noise adaptions. Results indicate that the volume of traffic noise negatively affects drivers’ performance in their routines. Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV, demonstrating that noise adaption is highly associated with drivers’ physiological status under noises. It is also found that noise adaption inclusion could raise the accuracy of classifications. Overall, the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions.