Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n  =  471) were fatal crashes, while the remaining 40% (n  =  321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

Association of risky driving behavior with psychiatric disorders among Iranian drivers: A case-control study

PURPOSE

This study aimed to investigate the possible association between psychological disorders and risky driving behavior (RDB) in Iran.

METHODS

This case-control study conducted in Shiraz, Iran in 2021. The case group included drivers with psychological disorders and the control group included those without any disorders. The inclusion criteria for selecting patients were: active driving at the time of the study, being 18 - 65 years old, having a driving license, having a psychological disorder including depression, bipolar disorder, anxiety spectrum disorder, or psychotic disorder spectrum confirmed by a psychiatrist, and completing an informed consent form. The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions. The inclusion criteria for selecting the healthy cases were: active driving at the time of the study, being 18 - 65 years old, having a driving license, lack of any past or present history of psychiatric problems, and completing an informed consent form. The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire. First, partition around medoids method was used to extract clusters of RDB. Then, backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.

RESULTS

The sample comprised of 344 (153 with psychological disorder and 191 without confirmed psychological disorder) drivers. Backward elimination logistic regression on total data revealed that share of medical expenditure ≤ 10% of total household expenditure (OR = 3.27, 95% CI: 1.48 - 7.24), psychological disorder (OR = 3.08, 95% CI: 1.67 - 5.70), and substance abuse class (OR = 6.38, 95% CI: 3.55 - 11.48) were associated with high level of RDB.

CONCLUSION

Substance abuse, psychological illnesses, and share of medical costs from total household expenditure were found to be main predictors of RDB. Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.