TY  - JOUR
PY  - 2023//
TI  - Aberrant driving behavior prediction for urban bus drivers in Taiwan using heart rate variability and various machine learning approaches: a pilot study
JO  - Transportation research record
A1  - Tsai, Cheng-Yu
A1  - Lin, Youxin
A1  - Liu, Wen-Te
A1  - Cheong, He-in
A1  - Houghton, Robert
A1  - Hsu, Wen-Hua
A1  - Iulia, Manole
A1  - Liu, Yi-Shin
A1  - Kang, Jiunn-Horng
A1  - Lee, Kang-Yun
A1  - Kuan, Yi-Chun
A1  - Lee, Hsin-Chien
A1  - Wu, Cheng-Jung
A1  - Joyce Li, Lok-Yee
A1  - Cheng, Wun-Hao
A1  - Ho, Shu-Chuan
A1  - Lin, Shang-Yang
A1  - Majumdar, Arnab
SP  - 1304
EP  - 1320
VL  - 2677
IS  - 3
N2  - 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. <br><br>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. <br><br>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%. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981221123802
ID  - ref1
ER  -