01861065

Component

https://doi.org/10.1177/03611981221123802

Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality (=80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84%?±?1.49% to 89.57%?±?1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.

Our study used existing records to conduct the study. All necessary approvals were received from and the content of informed consent was reviewed and approved by the Ethics Committee of the Taipei Medical University-Joint Institutional Review Board (TMU-JIRB: N202112077). The data owners signed the consent form and authorized the authors of this study to access their previously recorded data for the analysis.© National Academy of Sciences: Transportation Research Board 2022.

English

0000-0002-1639-4257

0000-0002-3820-7571

0000-0002-6891-6782

0000-0003-1281-8718

0000-0001-9979-5250

0000-0002-8437-301X

0000-0002-7850-4140

0000-0003-4705-5802

0000-0001-9316-4976

0000-0002-7557-8259

0000-0002-7443-2119

0000-0002-4833-2851

0000-0002-6332-7858

pp 1304-1320

2023-3

Transportation Research Record: Journal of the Transportation Research Board

Web

References (62)

Behavior; Bus drivers; Driving; Machine learning; Predictive models; Sleep

Taiwan

Highways; Safety and Human Factors

TRIS, TRB, ATRI

Oct 12 2022 3:02PM