Vehicle Driver Fatigue Research Articles

Heavy vehicle driver fatigue: Observing work and rest behaviours of truck drivers in Australia

The extant transport literature suggests that truck driver fatigue is a leading contributor in truck crashes. Various factors are known to affect a truck driver’s ability to comply with work and rest requirements, including long working hours, irregular work schedules, sleep deprivation and driving at night. In Australia, while control measures restrict the number of hours of work a truck driver may undertake without rest, they travel vast distances and work with a high level of autonomy. This makes it difficult for transport companies to monitor and manage truck drivers’ work and rest compliance; thereby relying on them to self-manage driver fatigue at designated rest-stops. However, there is little Australian research examining rest-stop behaviour of truck drivers in relation to driver fatigue compliance. To better understand how truck drivers manage driver fatigue, an exploratory participant observation study of truck drivers (N = 378) was undertaken at two Australian truck rest stops in rural New South Wales. The findings indicate that while most truck drivers comply with rest requirements regarding driver fatigue, load type, driver changeovers, access to parking, and time spent on work-related activities at the rest areas affects the time truck drivers spend at rest, and these factors are likely to increase the risk of truck drivers not complying with driver fatigue regulation. They are also likely to increase the number of truck drivers involved in fatigue related crashes.

Woozy Driving Detection

Abstract: Abstract-wooziness has been identified as one of the primary causes of injury or death in car accidents. As a result, it is proposed in this research work to develop an adaptive heavy vehicle driver fatigue and alertness modelbased on spectral bands by combining signal processing algorithms with soft computing techniques such as the neuro-fuzzy algorithm to estimate the driver cognitive state while driving a vehicle in a virtual reality (vr)-based dynamic simulator under monotonous driving conditions.As a result, it is proposed in this study to use soft computing approaches to reduce the amount of features and nonlinear supervised classification algorithms to classify them. The suggested adaptive model distinguishes between the driver's level of exhaustion by analysing brain responses to determine whether the driver is fatigued owing to task- induced variables or attitude/behaviour, and then the amount of fatigue is associated to sleepiness (i.e. Level of alertness towards driving). The adaptive model can also be used to warn drivers and regulators about impending catastrophes by optimizing the features of interface systems. The suggested system notifies the driver when he or she is fatigued or drowsy based on the identification of cognitive state and calculates the fatigue index and alertness level. The proposed method also assists the driverin being more alert and intuitive in order to avoid deadly road accidents.

Fatigue analysis and design of a motorcycle online driver measurement tool using real-time sensors

Work fatigue is an important aspect and is very influential in determining the level of accidents, especially motorbike accidents. According to WHO, almost 30% of all deaths due to road accidents involve two- and three-wheel­ed motorized vehicles, such as motorbikes, mopeds, scooters and electric bicycles (e-bikes), and the number continues to increase. Motor­cycles dominate road deaths in many low- and middle-income countries, where nine out of ten traffic accident deaths occur among motorcyclists, as in Indonesia. However, until now, in Indonesia, there has been no monitor­ing system capable of identifying fatigue in motorbike drivers in the transportation sector. This research aims to determine fatigue patterns based on driver working hours and create a sensor system to monitor fatigue measurements in real-time to reduce the number of accidents. The research began with processing questionnaire data with Pearson correlation, which showed a close relationship between driver fatigue and driving time and a close relationship between fatigue and increased heart rate and sweating levels. From calibration tests with an error of 3% and direct measurements of working conditions, it was found that two-wheeled vehicle driver fatigue occurs after 2-3 hours of work. With a measurement system using the Box Whiskers analysis method, respondents' working conditions can also be de­ter­mined, which are divided into 4 zones, namely zone 1 (initial condition or good condition), zone 2 a declining condition, zone 3 a tired condition and zone 4 is a resting condition. Hopefully, this research will identify fati­gue zones correctly and reduce the number of accidents because it can iden­tify tired drivers so they do not have to force themselves to continue working and driving their motorbikes. As a conclusion from this research, a measure­ment system using two sensors, such as ECG and GSR can identify work fatigue zones well and is expected to reduce the number of accidents due to work fatigue.

Method of biocontrol of vehicle driver fatigue

This article describes an example of negligence of drivers transporting passengers and methods of solving it using modern inventions. One of these troubles is driving a car and moving passengers by taxi driver in a tired state. Since not every driver can correctly assess their psycho-physical condition, so to do this, scientists began to create devices for tracking human behavior when he drives vehicle. The purpose of implementing driver fatigue monitoring systems is to ensure road safety and preserve lives and property of citizens. The use of these systems is to facilitate the work of emergency services and taxi company owners, taxi drivers and their passengers. In our article we want to touch on the problem of overwork, specifically taxi drivers, since their work activity is socially significant and non-compliance with the norms of work and rest periods can lead to tragic consequences. Modern taxi drivers often rely on a strong body of car and electronic gadgets in an unexpected situation on the road. Therefore, when driving a car, despite being overworked, they allow themselves to relax beyond the limit and dont react in time if an emergency occurs. We have studied options for implementing driver fatigue monitoring systems and offer to install them on a taxi car.

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Association between commercial vehicle driver at-fault crashes involving sleepiness/fatigue and proximity to rest areas and truck stops

IntroductionThere is ongoing concern at the national level about the availability of adequate commercial vehicle rest areas and truck stops for commercial vehicle drivers to rest or to wait for a delivery window. MethodsA retrospective case-control study was conducted to determine the association between the occurrence of sleepiness/fatigue-related (cases) vs. all other human factor-related commercial vehicle driver at-fault crashes (controls) and proximity to rest areas, weigh stations with rest havens, and truck stops. ResultsCommercial vehicle driver at-fault crashes involving sleepiness/fatigue were more likely to occur on roadways where the nearest rest areas/weigh stations with rest havens/truck stops were located 20 miles or more from the commercial vehicle crash site (Odds Ratio [OR]=2.32; Confidence Interval [CI] 1.615, 3.335] for 20–39.9 miles vs. <20 miles; and OR=6.788 [CI 2.112, 21.812] for 40+ miles) compared to commercial vehicle at-fault driver crashes with human factors other than sleepiness/fatigue cited in crash reports. Commercial vehicle driver at-fault crashes involving sleepiness/fatigue also were more likely to occur on parkways compared to interstates (adjusted OR=3.747 [CI 2.83, 4.95]), during nighttime hours (adjusted OR=6.199 [CI 4.733, 8.119]), and on dry pavement (adjusted OR 1.909, [CI 1.373, 2.655]). ConclusionsThe use of statewide crash data analysis coupled with ArcGIS mapping capabilities provided the opportunity to both statistically determine and to visualize the association between rest area/weigh station with rest haven/truck stop distance and the occurrence of commercial vehicle driver at-fault crashes involving sleepiness/fatigue. Implementation and evaluation of commercial vehicle employer policies and interventions such as the use of commercial vehicle driver fatigue alert systems may help to reduce fatigue and sleepiness in commercial vehicle drivers. These results can be used by state and local highway transportation officials to inform and increase truck parking availability, especially on parkways.

A mixed integer programming formulation and effective cuts for minimising schedule durations of Australian truck drivers

Transport companies seek to maximise vehicle utilisation and minimise labour costs. Both goals can be achieved if the time required to fulfil a sequence of transportation tasks is minimised. However, if schedule durations are too short drivers may not have enough time for recuperation and road safety is impaired. In Australia transport companies must ensure that truck drivers can comply with Australian Heavy Vehicle Driver Fatigue Law and schedules must give enough time for drivers to take the amount of rest required by the regulation. This paper shows how transport companies can minimise the duration of truck driver schedules complying with Australian Heavy Vehicle Driver Fatigue Law. A mixed integer programming formulation is presented and valid inequalities are given. Computational experiments show that these inequalities provide significant reduction in computational effort when using one of the most advanced commercial mixed integer programming solver.

The Australian Minimum Duration Truck Driver Scheduling Problem

Transport companies seek to maximise vehicle utilisation and minimise labour costs. Both goals can be achieved if the time required to fulfil a sequence of transportation tasks is minimised. However, if schedule durations are too short drivers may not have enough time for recuperation and road safety is impaired. In Australia transport companies must ensure that truck drivers can comply with Australian Heavy Vehicle Driver Fatigue Law and schedules must give enough time for drivers to take the amount of rest required by the regulation. This paper shows how transport companies can minimise the duration of truck driver schedules without violating Australian Heavy Vehicle Driver Fatigue Law. A mixed integer programming formulation is presented and effective cuts are given which provide significant reduction in computational effort.

Truck driver scheduling in Australia

In September 2008 new regulations for managing heavy vehicle driver fatigue entered into force in Australia. According to the new regulations there is a chain of responsibility ranging from drivers to dispatchers and shippers and thus, carriers must explicitly consider driving and working hour regulations when generating truck driver schedules. This paper presents and studies the Australian Truck Driver Scheduling Problem (AUS-TDSP) which is the problem of determining whether a sequence of locations can be visited within given time windows in such a way that driving and working activities of truck drivers comply with Australian Heavy Vehicle Driver Fatigue Law.

A method for optimizing waste collection using mathematical programming: a Buenos Aires case study

A method is proposed that uses operations research techniques to optimize the routes of waste collection vehicles servicing dumpster or skip-type containers. The waste collection problem is reduced to the classic travelling salesman problem, which is then solved using the Concorde solver program. A case study applying the method to the collection system in the southern zone of Buenos Aires is also presented. In addition to the typical minimum distance criterion, the optimization problem incorporates the objective of reducing vehicle wear and tear as measured by the physics concept of mechanical work. The solution approach, employing graph theory and mathematical programming tools, is fully described and the data correction process is also discussed. The application of the proposed method minimized the distance travelled by each collection vehicle in the areas studied, with actual reductions ranging from 10 to 40% of the existing routes. The shortened distances led in turn to substantial decreases in work done and therefore in vehicle wear and tear. Extrapolation of the results to the entire southern zone of Buenos Aires indicates potential savings for the civic authorities of more than US $200,000 per year in addition to the qualitative impacts of less traffic disruption, less vehicle driver fatigue and less pollution.

Truck Driving Environments and Their Influence on Driver Fatigue and Crash Rates

The development of a typology of commercial vehicle driving environments, estimate of the percentage of drivers falling into each type of driving environment, and driving environment effects on driver fatigue are described. A model of commercial motor vehicle driver fatigue, based on literature sources and focus groups of industry professionals, is proposed. Three driving-environment factors (regularity of time, trip control, and quality of rest), comprising 25 indicators, are included in the model. Data were collected via a nationwide survey of 502 randomly selected over-the-road commercial truck drivers. Data analysis revealed 12 driving-environment indicators to be good predictors of fatigue and crash outcomes. Various 2×2×2 driving-environment typologies were created by using different high-low combinations of these 12 indicators. A typology based on the single best predictors of fatigue and crash outcomes from each driving-environment factor was selected for examination (i.e., favorable and unfavorable combinations of driving the same hours, waiting longer than expected for loads, and starting the workweek tired). The percentage of drivers working in each type of driving environment ranged from 5.2 percent to 20.1 percent. Additionally, the typology was significantly related to frequency of close calls and perceptions of fatigue. The 12 driving-environment indicators collectively accounted for 5 percent and 23 percent of the variability in close calls and fatigue perceptions, respectively ( p ≥ .001), and 2 percent of the variability in crash involvement ( p ≥ .07). Implications for fatigue management are also discussed.