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Time distribution of sleep-related accidents and traffic during the day. Heavy line: total counts of hourly sleep-related accidents during the five years considered in the study. Gray area: percent traffic distribution of the traffic density during the day. Thin line: relative risk of sleep-related accidents (see the text for the definition)
Source publication
Sleepiness has been identified as a significant risk factor for vehicle accidents, and specific surveys are needed for Italy. The aim of this study was to assess incidence and characteristics of sleep-related vehicle-crashes on Italian highways. The database of the Italian National Institute of Statistics (1993-1997) was the source for the survey (...
Citations
... 19 From the standpoint of analytic methods, various regression type models have been used in fatigue and drowsiness accidents. 15,19,[21][22][23][24] In regression modelling, the relationships between dependent and independent variables should be defined before modelling, also the model estimation will cause erroneous inferences in case the assumptions do not hold. 25 Some algorithms such as ANN and SVM also have a good ability to predict and classify data, but they cannot provide a proper interpretation of the outputs for analysts and look like a black box difficult to interpret and understand individualized feedback to analysts. ...
Background:
Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents.
Methods:
The Classification and Regression Tree method (CART) was applied 11,392 drivers were involved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clustering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method.
Results:
The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models.
Conclusions:
The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities.
... According to the US National Highway Traffic Safety Administration, drowsiness and fatigue have been identified as causal factors in 1.2-1.6% of all police-reported crashes and 3.6% of all fatal crashes [25]. Another study has shown that approximately 18.0% of traffic deaths are related to sleepy driving [26]. A previous study conducted by our research group has shown that a significant proportion of Brazilian truck drivers suffer from excessive sleepiness, chronic sleep debt and poor quality of sleep [17]. ...
Abstract
Objective: We investigated the relationships among parameters related
to accident involvement, sleep patterns and health habits of shift-working
Brazilian truck drivers.
Methods: In this cross-sectional study, 205 Brazilian truck drivers were
invited and accepted to complete our survey based on the validated
structured “UNIFESP Sleep Questionnaire”. A multiple correspondence
analysis was used to assess the clustering of evaluated potential categorical
variables with involvement in automobile accidents, aiming to examine
associations between these variables.
Results: Our results generated two distinct truck drivers’ profiles. For
the first profile, we observed that drivers who reported involvement in
accidents appeared similar to those who reported drug usage, driving
more than 14 to 19 hours without rest, excessive sleepiness, falling asleep
while driving and sleep complaints. Conversely, the second profile showed
that subjects who were not involved in accidents were similar to subjects
who reported no sleep complaints or excessive sleepiness, did not falling
asleep while driving and did not use drugs. We have also observed that the
variable contributing the most to these two profiles was overnight travel,
followed by falling asleep while driving and sleep complaints. Our data also
demonstrated that exposure to accidents was 4 times higher for drivers
who habitually drive during the night. We have also observed a protective
effect in terms of accident involvement for drivers who usually work fewer
than 12 hours per day.
Conclusion: Our results highlighted how adequate sleep habits, as well as,
the consequences related to sleep disturbances, are associated with drug
consumption and accident involvement by truck drivers.
Keywords: Accidents; Fatigue; Overnight travel; Drivers; Drugs; Falling
asleep
... Among them, 3.2% of accidents are related to sleepiness based on police reports. 20 Considering traffic density, the highest frequency of sleepiness-related accidents happened during the peak hours of sleepiness of circadian rhythm. Also, sleepiness-related car crashes have higher mortality than the accidents due to other causes. ...
... Our study revealed that sleepiness is one of the risk factors for car accidents but currently it is often ignored. 20 The percentage of accidents caused by sleepiness in this study is similar to another study that was done in the US, but is lower than the percentage obtained in a study conducted in the UK. ...
Road traffic accidents are one of main problems in Iran. Multiple factors cause traffic accidents and the most important one is sleepiness. This factor, however, is given less attention in our country. Road traffic accidents relevant to sleepiness are studied.
In this cross-sectional study, all road traffic accidents relevant to sleepiness, which were reported by police, were studied in Tehran province in 2009.
The risk of road traffic accidents due to sleepiness was increased by more than sevenfold (odds ratio = 7.33) in low alertness hours (0:00-6:00) compared to other time of day. The risk of road traffic accidents due to sleepiness was decreased by 0.15-fold (odds ratio = 0.15) in hours with maximum of alertness (18:00-22:00) of circadian rhythm compared to other time of day.
The occurrence of road traffic accidents due to sleepiness has significant statistical relations with driving during lowest point of alertness of circadian rhythm.
... According to the US National Highway Traffic Safety Administration, drowsiness and fatigue have been identified as causal factors in 1.2-1.6% of all police-reported crashes and 3.6% of all fatal crashes [25]. Another study has shown that approximately 18.0% of traffic deaths are related to sleepy driving [26]. A previous study conducted by our research group has shown that a significant proportion of Brazilian truck drivers suffer from excessive sleepiness, chronic sleep debt and poor quality of sleep [17]. ...
... The results showed clear time-dependent characteristics in sleep propensity; the highest propensity for sleep occurred from midnight to early morning, with a secondary peak in the midafternoon, and the lowest propensity for sleep occurred at around 08:00PM, which corresponded to about 2 to 3 hours before habitual bed time. Considering that sleepiness may cause human error and a deterioration in mental performance, reported timedependent characteristics in traffic accidents 24) and mistakes in monitoring work 25) were closely concordant with the results reported by Lavie et al. 23) . ...
This review introduces a variety of human circadian rhythms including physiological processes and mental and physical performances, with reference to real-life situations. Circadian rhythms play a role in physiological processes, such as core body temperature and plasma mela-tonin, which are recognized as the body clock. As humans are diurnal organisms, mental performance declines primarily at night, secondarily in the early afternoon; this is consistent with risks of traffic and industrial accidents. Physical performance is composed of various fitness components and generally reaches its peak and nadir at around evening and early morning, respectively. Exceptions to this are body balance control and accuracy, both of which require brain function. Although maximal oxygen consumption (V ・ O 2 max) measured in the laboratory shows a constant value independent of the time of day, actual endurance capacity might be determined by core body temperature at the beginning of exercise, thermoregulatory response, and environmental temperature and humidity, all of which vary with the time of day. As the most powerful factor affecting the human circadian clock is bright light, physical exercise may be one factor entraining the human circadian pacemaker. However, experimental evidence has suggested that exercise itself has little or no influence on shifting the human master clock. Although further studies are required, recent studies have demonstrated that physical exercise at a certain time of day specifically improves physical performance at the same time, which might be independent of the master clock.
... A number of factors consistently emerge in the international literature as contributors to driver crashes. Driver characteristics include age, gender, license status [23], [12], [24], [25], driving experience, consumption of alcohol or drugs [10], [11], [13], [8], fatigue [26], [3], [18], [19], inattention or not wearing seat belts [20]. ...
... According to traffic police reports, a series of personal characteristics and road variables were investigated. As certain circumstances were rare, the factor levels for the analysis of variance were grouped as in previous investigation [3]: ...
... As to the influence of time of the day, our data put into evidence clear circadian and semi-circadian effects with evidence of high incidence of sleep-related accident around 0 and 3 a.m. and in the early afternoon. Such a pattern is highly correlated with the well-known circadian and semi-circadian rhythm of alertness-sleepiness, reported by several laboratories [3]. Sleepiness is a typical manifestation of the biological need of sleep and increased sleepiness is associated with a decrement in reaction time, psychomotor coordination, information processing and decision making which influence the probability of having accidents. ...
With the aim of increasing information about risk factors for crashes in the area of Parma, North Italy, a total of 1489 road accidents occurred in the year 2008 was analyzed. Logistic regression was used to evaluate the association between drivers, accidents characteristics and accident outcomes (killed, severely, and mildly injured). Age classes much involved in road crash were 26-25 and 36-35 years. Men were much frequently responsible for accident than women. The hourly distribution of crash for working days, Saturday and Sunday showed that the prevalence was higher during the late night hours (0-3 on Sunday and 20-23 on Saturday, respectively). The youngest age class was involved in a greater number of accidents especially on 0-3 time of day class. About half of road crashes was directly attributed to violations. High-speed, alcohol and drug abuse affected only a small portion of cases. The highest combined risk of dying or being severely injured was found in males, driving a motorcycle. These results will influence transport and local safeties measures and policies, which will change inappropriate behaviors of drivers and protect the least experienced road users.
... As modern trends reduce sleep opportunities, and lead us to travel greater distances at all times of the day, the problem of sleepy driving only looks set to grow (Dinges, 2011). Information on the importance of sleepy driving as a contributor to road accidents is available from accident analyses which aim to characterize and identify sleepy driving accidents using information in national databases (Garbarino et al., 2001). These analyses consistently confirm that the main temporal and environmental risk factors are early hours driving and long, monotonous driving tasks. ...
The current study tests, updates and expands a model of factors associated with sleepy driving, originally based on a 1997 survey of accident-involved Norwegian drivers (Sagberg, F., 1999. Road accidents caused by drivers falling asleep. Accident Analysis & Prevention 31, 639-649). The aim is to establish a robust model to inform measures to tackle sleepy driving. The original questions on (i) tiredness-related accidents and (ii) incidents of sleep behind the wheel in the last 12 months were again posed in 2003 and 2008, in independent surveys of Norwegian drivers involved in accidents reported to a large insurance company. According to those drivers at-fault for the accident, tiredness or sleepiness behind the wheel contributed to between 1.9 and 3.9 per cent of all types of accident reported to the insurance company across these years. Accident-involved drivers not at fault for the accident reported a reduction in the incidence of sleep behind the wheel for the preceding year, decreasing from 8.3 per cent in 1997 to 2.9 per cent in 2008. The reasons for this are not clear. According to logistic regression analysis of survey responses, the following factors were robustly associated with road accidents involving sleepy driving: driving off the road; good road conditions; longer distance driven since the start of the trip; and fewer years with a driving licence. The following factors are consistently associated with reports of sleep behind the wheel, whether or not it leads to an accident: being male; driving further per year; being younger; and having sleep-related health problems. Taken together these findings suggest that young, inexperienced male drivers who drive long distances may be a suitable target for road safety campaigns aimed at tackling sleepy driving.
... A number of factors consistently emerge in the international literature as contributors to driver crashes. Driver characteristics include age, gender, license status [23], [12], [24], [25], driving experience, consumption of alcohol or drugs [10], [11], [13], [8], fatigue [26], [3], [18], [19], inattention or not wearing seat belts [20]. ...
... According to traffic police reports, a series of personal characteristics and road variables were investigated. As certain circumstances were rare, the factor levels for the analysis of variance were grouped as in previous investigation [3]: ...
... As to the influence of time of the day, our data put into evidence clear circadian and semi-circadian effects with evidence of high incidence of sleep-related accident around 0 and 3 a.m. and in the early afternoon. Such a pattern is highly correlated with the well-known circadian and semi-circadian rhythm of alertness-sleepiness, reported by several laboratories [3]. Sleepiness is a typical manifestation of the biological need of sleep and increased sleepiness is associated with a decrement in reaction time, psychomotor coordination, information processing and decision making which influence the probability of having accidents. ...
In order to identify the major risk factors for crashes in the area of the Province of Parma, North Italy, Police Reports of 1,489 road accidents occurring in 2008 were analysed. Logistic regression was used to evaluate the association between driver and accident characteristics with accident severity. Age groups most involved in road crashes were 26-35 year olds and 36-45 year olds. Women were less frequently involved in accidents than men. The hourly distribution of accidents for working days, Saturdays and Sundays showed that the main differences were evident late at night (12 a.m.-3 a.m. on Sunday and 4-7 a.m. on Saturday, respectively). The youngest age group was involved in a greater number of accidents, which greatly increased between 12 a.m.-3 a.m. As for the causes of road crashes, about half of them were attributed to violations, while speeding was found to be the second most frequent road crash cause, surpassing alcohol and drug abuse. The risk of involvement in severe rather than non-severe accidents was significantly related to vehicle type, in that motorcycle drivers suffered more serious injuries in comparison to car or lorry drivers. These data should influence local transport safety measures and policies.
... It has also been shown that drivers' objective sleepiness, measured using maintenance of wakefulness tests or multiple sleep latency tests, predict an increased risk of car-accidents and impair driving performance [9][10][11][12][13]. In addition, accidents caused by drowsy driving are likely to have disastrous outcomes [1,14], leading to elevated economic costs [15]. ...
We explored differences between professional and non-professional drivers in terms of the factors associated with preferences for generally accepted, effective countermeasures for sleepiness at the wheel--i.e., napping and drinking coffee.
We performed a cross-sectional questionnaire survey. Data from professional (n = 716) and non-professional (n = 3365) drivers were used for analyses.
The results showed that professional drivers experienced drowsy driving and traffic accidents due to falling asleep more often than non-professional drivers. Multiple logistic regression analyses showed that variables which may act as aggravating factors for sleepiness (i.e., engagement in shift-work and insufficient sleep) were associated with preferences for these countermeasures among non-professional drivers. In contrast, among professional drivers, being male and having experienced traffic accidents due to drowsy driving were associated with a preference for napping, while longer annual driving distances and shorter periods after the acquisition of driving licenses were associated with drinking coffee.
Our results suggest that non-professional drivers are likely to take these effective countermeasures when they feel or have the potential to experience sleepiness at the wheel. However, this tendency was not observed in professional drivers, and it is speculated that they do not use naps as a countermeasure until they have experienced traffic accidents due to drowsy driving. Sleep education for professional drivers and their employers is desirable for preventing drowsy driving-related traffic accidents.
... The relative frequency of these accidents peaks in early morning and mid afternoon, which are synchronized to points in the circadian cycle associated with sleepiness (e.g. Garbarino et al., 2001;Horne and Reyner, 1995;Maycock, 1996). This phenomenon is also observed in the patterns of errors made by individuals reading gas meters (Mitler and Miller, 1996). ...
Performance monitoring is an essential function involved in the correction of errors. Deterioration of this function may result in serious accidents. This function is reflected in two event-related potential (ERP) components that occur after erroneous responses, specifically the error-related negativity/error negativity (ERN/Ne) and error positivity (Pe). The ERN/Ne is thought to be associated with error detection, while the Pe is thought to reflect motivational significance or recognition of errors. Using these ERP components, some studies have shown that sleepiness resulting from extended wakefulness may cause a decline in error-monitoring function. However, the effects of sleep inertia have not yet been explored. In this study, we examined the effects of sleep inertia immediately after a 1-h daytime nap on error-monitoring function as expressed through the ERN/Ne and Pe. Nine healthy young adults participated in two different experimental conditions (nap and rest). Participants performed the arrow-orientation task before and immediately after a 1-h nap or rest period. Immediately after the nap, participants reported an increased effort to perform the task and tended to estimate their performance as better, despite no objective difference in actual performance between the two conditions. ERN/Ne amplitude showed no difference between the conditions; however, the amplitude of the Pe was reduced following the nap. These results suggest that individuals can detect their own error responses, but the motivational significance ascribed to these errors might be diminished during the sleep inertia experienced after a 1-h nap. This decline might lead to overestimation of their performance.
