Reduction Of Traffic Accidents Research Articles

Impact of COVID-19 pandemic on road traffic injuries in Iran: An interrupted time-series analysis.

Globally, the COVID-19 pandemic has affected the number of road accidents and deaths caused by them. The present study aimed to identify the effect of this epidemic on traffic accidents and their casualties in Iran. In this study, Interrupted Time Series Analysis (ITSA) was used in a semi-experimental design to measure the impact of the restrictive policies of COVID-19 on road accidents. Data were collected retrospectively from the Iran Red Crescent Society data set for 31 provinces from March 2017 to February 2022. The information related to the number of road accidents, injuries, deaths, and deaths in the hospital was collected. The Newey-West method is used for estimation. Statistical analyses were carried out using R software version 3.6.1. Since February 2020 in Iran, the reduction in the number of road accidents and the number of injuries and deaths in these accidents was significant at 5% but the reduction of deaths in the scene and hospital was significant at 10%. In general, for all variables, the reduction trend was established only in the first months, and then it had an upward trend. In the early months of the COVID-19 epidemic in Iran, the number of road accidents and their casualties decreased. Policies restricting traffic, quarantine, and fines for violators can be reasons for changing people's behavior and travel patterns and also lead to a reduction in traffic accidents and fatalities. Such studies can explain the importance of the policies in changing behavioural patterns and can be used as a guide in future policies.

Determinants of Road Traffic Crash Fatalities: A Case of Road Accidents in India, 2004–2018

The purpose of this study is to determine the factors that influence deaths in road accidents in India. The study uses panel econometric methodologies such as the fixed effects model, random effects model, and Hausman–Taylor instrumental variable technique to achieve this goal. Unobserved heterogeneity between states/union territories (UTs) and time is accounted for by time-fixed and state-fixed effects. Based on the panel data set from Indian states/UTs for the period 2004–2018, the study confirms previous results that motorisation, population, urbanisation and income level are all potential contributors to road accidents and fatalities. It also shows that spending on health care and law enforcement appears to help the Indian states minimise mortality in road traffic accidents. On the one hand, spending on health care facilities can provide health-related assistance to persons who have been involved in traffic accidents; on the other hand, spending on police increases the enforcement of rules, resulting in a reduction in traffic accidents. Thus, to lessen the economic and financial cost of road accidents in the country, the government should focus on boosting both per capita health expenditure and per capita police expenditure. JEL Codes: C23, C26, H51, I18, L92, O18, R41, R42

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Evaluating the impact of minimum unit pricing for alcohol on road traffic accidents in Scotland after 20 months: An interrupted time series study.

On 1 May 2018, Scotland implemented Minimum Unit Pricing (MUP) of £0.50 per unit of alcohol with the aim to lower alcohol consumption and related harms, and reduce health inequalities. We measured the impact of MUP on the most likely categories of road traffic accidents (RTAs) to be affected by drink-driving episodes (fatal and nighttime) up to 20 months after the policy implementation. Further, we checked whether any association varied by level of socio-economic deprivation. An interrupted time series design was used to evaluate the impact of MUP on fatal and nighttime RTAs in Scotland and any effect modification across socio-economic deprivation groups. RTAs in England and Wales (E&W) were used as a comparator. Covariates representing severe weather events, bank holidays, seasonal and underlying trends were adjusted for. In Scotland, MUP implementation was associated with 40.5% (95% confidence interval: 15.5%, 65.4%) and 11.4% (-1.1%, 24.0%) increases in fatal and nighttime RTAs, respectively. There was no evidence of differential impacts of MUP by level of socio-economic deprivation. While we found a substantial increase in fatal RTAs associated with MUP, null effects observed in nighttime RTAs and high uncertainty in sensitivity analyses suggest caution be applied before attributing causation to this association. There is no evidence of an association between the introduction of minimum unit pricing for alcohol in Scotland and a reduction in fatal and nighttime road traffic accidents, these being outcome measure categories that are proxies of outcomes that directly relate alcohol consumption to road traffic accidents.

Fatigue State Detection for Tired Person in Presence of Driving Periods

Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Algorithms have been developed to address this issue by detecting fatigue and alerting the driver to dangerous condition. The major problem of the developed algorithms is their accuracy, well as the time required to detect fatigue status and alert the driver. The accuracy and time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data gathered from the deriver's brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolution Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time (TT) when compared to all other recent and efficient fatigue detection algorithms.

SCL-GCN: Stratified Contrastive Learning Graph Convolution Network for pavement crack detection from mobile LiDAR point clouds

Accurate pavement crack detection is important for routine maintenance of pavements and reduction of possible traffic accidents. Most existing rule- or learning-based point-level approaches cannot achieve high detection accuracy and efficiency owing to the disorderly arrangement, scattered intensities, diverse crack structures, large data volumes, and complex annotation of mobile laser scanning (MLS) point clouds. To address these issues, we developed SCL-GCN, a Stratified Contrastive Learning Graph Convolution Network with a novel dual-branch architecture for MLS-point cloud-based pavement crack detection. First, a multi-scale graph representation construction module was designed based on a stratification strategy. This module creates strengthened spaces for the raw pavement point cloud and its downsampled subset, from which adjacency matrices and initial representations are generated. The stratification strategy samples neighbors densely in the raw point clouds and sparsely in the downsampled subset to form the neighborhood for each point, utilizing long-range contexts to increase the effective receptive field while lowing the extra computation. Next, a graph feature contrastive learning module is proposed to take advantage of stratified features. This module supervises the learning process of the two branches to avoid learning bias caused by an imbalanced data distribution, promoting convergence and improving performance. The experimental results show that the developed SCL-GCN model outperforms state-of-the-art methods. With a training/testing ratio of only 1:6 and an overall training time of less than 70 min, the average precision, recall, and F1-score of the SCL-GCN reached 75.7%, 75.1%, and 75.2%, respectively.

Clinico-Etiological Profile of Children Admitted with Head Injury in a Tertiary Health Care Centre During the COVID Pandemic

Abstract Background The mechanism of injury, type of pathology, mode of management and specific problems, in the pediatric age group make these a unique population. The COVID-19 pandemic and lockdown caused a significant reduction in the number of road traffic accidents during the same period and the resultant number of head injuries in children. Methods This was a descriptive study of 76 consecutive pediatric patients admitted with head injury between June and December 2020. Children under 18 years with head injury admitted in our hospital were included. Results The most common etiology of pediatric head injury was found to be fall from height (61.8%) followed by road traffic accidents (27.6%). The most common age group affected was 1 to 5 years with a mean of 6.3 ± 5 years. Road traffic accidents were commonly seen in the 15 to 18 age group. The Glasgow Outcome Score (GOS) of 1 (death) was seen in one patient (1.3%) and low disability in 98.7% of patients. Conclusion Falls formed the most important cause of pediatric head injury during this pandemic, and carefulness on the part of parents can help avoid dangerous consequences for the children. Recovery with minimal disability was observed in approximately all cases in this study. The number of severe traumatic brain injury was very low in this study. This can be attributed to the COVID-19 pandemic causing significant reduction in road traffic accidents and the number of severe head injury

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

Despite the measures put in place in different countries, road traffic fatalities are still considered one of the leading causes of death worldwide. Thus, the reduction of traffic fatalities or accidents is one of the contributing factors to attaining sustainability goals. Different factors such as the geometric structure of the road, a non-signalized road network, the mechanical failure of vehicles, inexperienced drivers, a lack of communication skills, distraction and the visual or cognitive impairment of road users have led to this increase in traffic accidents. These factors can be categorized under four headings that are: human, road, vehicle factors and environmental road conditions. The advent of machine learning algorithms is of great importance in analysing the data, extracting hidden patterns, predicting the severity level of accidents and summarizing the information in a useful format. In this study, three machine learning algorithms for classification, such as Decision Tree, LightGBM and XGBoost, were used to model the accuracy of road traffic accidents in the UK for the year 2020 using their default and hyper-tuning parameters. The results show that the high performance of the Decision Tree algorithm with default parameters can predict traffic accident severity and provide reference to the critical variables that need to be monitored to reduce accidents on the roads. This study suggests that preventative strategies such as regular vehicle technical inspection, traffic policy strengthening and the redesign of vehicle protective equipment be implemented to reduce the severity of road accidents caused by vehicle characteristics.

Security Challenges Facing Blockchain Based-IoV Network: A Systematic Review

The Internet of Vehicles (IoV) is an innovative concept aimed at addressing the critical problem of traffic congestion. IoV applications are part of a connected network that collects relevant data from various smart sensors installed in connected vehicles. This information is freely and easily exchanged between vehicles, which leads to improved traffic management and a reduction in traffic accidents. As the IoV technology continues to grow, the amount of data collected will increase, presenting new challenges for data privacy and security. The use of blockchain technology has been proposed as a solution, as its decentralized and distributed architecture has been proven reliable with cryptocurrencies such as Bitcoin. However, studies have shown that blockchain alone may not be sufficient to address privacy and security concerns, and there are currently no tools available to evaluate its performance in an IoV simulation environment. This research aims to provide a comprehensive review of the challenges associated with the implementation of blockchain technology in the IoV context.

Impact of COVID 19 lockdown on Medico Legal Cases in a Tertiary Care Hospital in North Kerala

Background: The consequence of Covid 19 lockdown on medicolegal cases was variable throughout the world.Methodology: A descriptive cross sectional study was conducted in a tertiary care hospital setting and otherpublic health institutions in the same district with the objective of comparing thetotal number of medico legalcases, any increase or decrease in the unnatural (accidental, suicidal and homicidal) versus natural deaths and alsowhether any increase or decrease of coronary artery disease among the brought dead natural cases during pre andthe lock down period.Results: Among the total number of medicolegal cases came for autopsy (237), 65.8% were during pre lock downperiod and 34.2% were during lockdown period. This indicates a significant reduction in the number of casesduring lockdown compared to pre lockdown in both type of institutions. This reduction in number of cases wereobserved in all the type of cases as indicated by reduction in unnatural deaths (30.2%versus 69.8%), small reductionin natural deaths (46.3% versus 53.7%), accidental deaths (26% versus 74%), suicidal deaths (36.2% versus 63.8%),reduction in Road traffic accidents(26.3%versus 73.7%), deaths following coronary artery disease (42.9%versus57.1%). The age distribution of deaths due to coronary artery disease during the pre lockdown period was in arange of 28 – 85 years and during lockdown period was 33- 78 years with a median of 48 years in both groups.Conclusion: Regardless of the type of, there is reduction in the number of medicolegal cases during lockdowncompared to pre lockdown in both the types of institutions.

Driving Fatigue Effects on Cross-Frequency Phase Synchrony Embedding in Multilayer Brain Network

Driver fatigue has been intensively investigated for recent decades, nevertheless the underlying neural mechanism remains unclear. This study explored the cross-frequency coupling (CFC) between slow and fast oscillations in a multilayer brain network description of the functional brain network. Specifically, we compared the topological characteristics of the CFC embedded multilayer brain networks in the vigilant and fatigue states. From the 24-channel EEG recorded on 20 subjects, we found that the CFC of the fatigue state was elevated, especially in the beta-gamma coupling and in the frontal pole, frontal, and parietal regions. Results also revealed profound differences in the topology of the multilayer brain network between the vigilant and fatigue states, particularly the significant increases in the global and local efficiencies of the multilayer network in the fatigue state that were closely related to the behavioral performance, i.e., the reaction time. What is more, a graph neural network (GNN) was developed for imitating the features of the within-frequency sub-networks diffused through the CFC to detect fatigue with a satisfactory classification accuracy (96.23%). The proposed approach could enhance our understanding about neural coordination across frequencies in driver fatigue and would facilitate the fatigue-related studies for a better understanding about the underlying mechanism and ultimately a traffic accident reduction.

Alkoholio vartojimo ribojimų įtaka negatyviems eismo įvykiams Lietuvos magistraliniuose keliuose

The article uses statistical data from the Lithuanian Road Police Service on traffic accidents, speeding, drinking and driving (DUI), and people injured in traffic accidents on Lithuanian highways, i.e., negative traffic accidents in the period of 2000–2018. The aim of the work is to analyse the dynamics of traffic collisions and circumstances related to them. To achieve this aim, the negative accident data recorded on Lithuanian highways was converted into a 1-mile (8 km) unified system by grouping roads according to their length: small, average, large, and the largest roads. Time series AR(1) models were found for most of the cases. By employing statistical methods, it was identified that the 2008 law restricting the sale and advertising of alcohol led to the following results: a reduction in traffic accidents and the number of intoxicated drivers on all roads, although at the same time there was an increase in speeding. The number of people injured on small roads has increased.

Agent-based modelling approach for autonomous vehicle influence on countries’ welfare

The inevitable transition from conventional to self-driving vehicles is a large-scale process involving a series of interactions and related to major challenges for human society. Autonomous vehicles (AVs) considered as a future of road transport, have the potential to significantly reduce transport costs, improve road safety by significantly reducing road accidents and fatalities, make vehicles more environmentally friendly, reduce the travel time and increase the number of passengers. Taking into account all the benefits of AVs, the rapid development of AVs in all countries is becoming a very important factor. This research considers the assessment of the households welfare influence on the development of AVs. To ensure realistic future scenarios modelling was supported by empirical data. The obtained modelling results made it possible to predict a significant reduction in traffic accidents and environmental pollution, and to consider some policy recommendations to promote the development and a broader range introduction of AVs. The method of agent-based modelling was used to develop the market penetration simulation, which allowed quantifying consumer motives and opportunities in choosing AVs, the development trends of AVs, and providing policy recommendations not only for aggregated country level situations, but for households and individuals as well. The proposed model and results may be useful for planners and researchers working on long-term solutions related to safer and less polluting AVs and predicting purchasing behaviour of households.

A Cross-sectional Study of Road Traffic Safety Knowledge, Attitude, and Practice among Emergency Department Staff

Background and Purpose: Emergency Department (ED) staff are frequently faced with road traffic injury victims and also play role as various road users. This study was conducted to investigate the knowledge, attitude, and practices of ED staff towards road safety at referral trauma hospitals of Tabriz.
 Materials and Methods: This cross-sectional study was conducted in 2020 in the referral trauma hospitals of Tabriz. Data were collected using a standard questionnaire. Census sampling was used to select the participants. Descriptive statistics, Pearson’s correlation test and logistic regression model were used for the data analysis using SPSS Software.
 Results: The mean score of the ED staff in traffic safety knowledge, attitude and practices was 9.73 ± 2.28 out of 15, 95.3±11.4 out of 118, and 61.3±8 out of 76, respectively. The staff’s traffic safety knowledge was associated with their attitude and practices. Moreover, the univariate logistic regression modeling results revealed that participants' traffic safety attitude (OR= 0.96 (95% CI= 0.93-0.99)) and practices (OR= 0.95 (95% CI= 0.91-0.99)) were, independently, correlated with a reduction in road traffic accidents.
 Conclusions: The ED staff’s attitudes towards traffic safety had a positive correlation with their practices and also accident experience. Nonetheless, after adjusting the data in terms of knowledge and attitude, road traffic safety practice was not associated with participants' accident experience. It is crucial to consider road safety education in colleges to promote safety knowledge of graduate individuals which will have conspicuous results in risk reduction.

Drivers’ Knowledge of and Preferences for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) offer many potential advantages, including improved traffic flow, reduction of traffic accidents, and increased freedom for adolescents and adults with restricted mobility. However, successful implementation of CAVs depends on several factors, especially acceptance and preferences by people. Specifically, during the earlier stage of deployment, CAVs will have to share the roads with human-driven vehicles (HDVs), which requires communication between CAVs and HDVs regarding their intentions and future actions. Therefore, as a first step in our research program, we conducted a survey of 182 U.S. drivers to assess their knowledge of CAVs and their thoughts about implementation. We report the survey results, accompanied by our interpretations.

Beyond the Steering Wheel: Exploring the Frontiers of Automotive Technology

This research explores the transformative impact of emerging automotive technologies on safety, sustainability, and the future of transportation. Leveraging empirical data, including accident analysis, consumer surveys, and qualitative research, the study investigates the adoption and implications of autonomous vehicles, the shifting consumer preferences towards electric propulsion, and the evolving expectations regarding vehicle connectivity. The findings reveal a significant correlation between the adoption of autonomous vehicles and a substantial reduction in traffic accidents. The study reports a 23% decrease in accidents following the introduction of autonomous vehicles, emphasizing their potential to enhance road safety and reduce associated healthcare costs. Consumer preferences are shifting towards electric vehicles (EVs), with 68% of surveyed vehicle owners expressing interest in EVs as their next car purchase. Environmental concerns (43%) and cost savings (32%) are the primary motivators, highlighting the need for increased investment in EV infrastructure and incentives to support sustainable transportation. Qualitative data analysis identifies connectivity features as pivotal in redefining the driving experience. Enhanced infotainment systems, advanced driver-assistance features, and seamless smartphone integration are among the key features driving consumer expectations. The study underscores the importance of integrating advanced technology into future vehicle models to meet evolving consumer demands. The implications of these findings are significant for various stakeholders, including policymakers, automotive manufacturers, and consumers. The study recommends continued investment in autonomous vehicle technology, expansion of EV infrastructure, innovation in connectivity features, consumer education, and industry collaboration to shape a safer, more sustainable, and technologically advanced future of transportation.

The Road Ahead: A Comprehensive Look at Cutting-Edge Automotive Technology

The rapid evolution of automotive technology is reshaping the landscape of transportation, offering promising solutions to enhance road safety, promote sustainability, and redefine the driving experience. This comprehensive study investigates three critical facets of cutting-edge automotive technology: autonomous vehicles, electric propulsion, and vehicle connectivity. The results indicate a significant correlation between the adoption of autonomous vehicles and a remarkable 23% reduction in traffic accidents, underscoring their potential to revolutionize road safety and mitigate healthcare costs. Moreover, a survey of 1,000 vehicle owners reveals a notable 68% interest in electric vehicles (EVs) as their next car purchase, primarily driven by environmental concerns (43%) and cost savings (32%).Qualitative analysis highlights the pivotal role of connectivity features in modern vehicles, with consumers prioritizing enhanced infotainment systems, advanced driver-assistance features, seamless smartphone integration, and other technological innovations that elevate the driving experience. This study's findings hold profound implications for stakeholders across the automotive industry. Manufacturers must continue innovating to meet the burgeoning demand for advanced technology, while policymakers must navigate the regulatory complexities associated with autonomous vehicles and invest in EV infrastructure development. The study offers a holistic view of how emerging automotive technologies are poised to shape safety, sustainability, and the future of transportation. These transformative trends emphasize the need for collaboration, innovation, and responsive policy-making to harness the full potential of cutting-edge automotive technology for a safer, more sustainable, and technologically advanced future of mobility

SARIMA Modelling Approach for Forecasting of Traffic Accidents

To achieve greater sustainability of the traffic system, the trend of traffic accidents in road traffic was analysed. Injuries from traffic accidents are among the leading factors in the suffering of people around the world. Injuries from road traffic accidents are predicted to be the third leading factor contributing to human deaths. Road traffic accidents have decreased in most countries during the last decade because of the Decade of Action for Road Safety 2011–2020. The main reasons behind the reduction of traffic accidents are improvements in the construction of vehicles and roads, the training and education of drivers, and advances in medical technology and medical care. The primary objective of this paper is to investigate the pattern in the time series of traffic accidents in the city of Belgrade. Time series have been analysed using exploratory data analysis to describe and understand the data, the method of regression and the Box–Jenkins seasonal autoregressive integrated moving average model (SARIMA). The study found that the time series has a pronounced seasonal character. The model presented in the paper has a mean absolute percentage error (MAPE) of 5.22% and can be seen as an indicator that the prognosis is acceptably accurate. The forecasting, in the context of number of a traffic accidents, may be a strategy to achieve different goals such as traffic safety campaigns, traffic safety strategies and action plans to achieve the objectives defined in traffic safety strategies.

Possibilities for reduction of energy consumption by replacing public lighting with LED lighting: Case study of Priboj municipality

Electric energy consumed by the public lighting represents a significant item in the city public sector energy consumption structure, which includes public buildings, public transportation and public lighting. Public lighting system is one of the most important public utility activities which is under control of local governments, which pay the costs of electricity, as well as maintenance costs. Public lighting affects the quality of life of citizens in cities through, among other things, reduction of traffic accidents by up to 35%, personal safety and reduction of social approaches. Cities and municipalities in Serbia are predominately using sodium and mercury bulbs for public lighting, but have been gradually replacing them with LED bulbs in the recent years. LED technology is a satisfactory, cost-effective solution due to low energy consumption, long service life, reduced investment and maintenance costs. Thus, there is not only a reduction in energy consumption of up to 70%, but also a large reduction in CO2 emissions. The modernization of the public lighting system is, in addition to being a useful means of achieving significant energy savings, also a means of improving the quality of lighting itself. The paper shows how much it is possible to reduce electricity consumption, and thus CO2 emissions, by replacing existing lighting with LED lighting. There are 2530 light bulbs in the public lighting system of the municipality of Priboj. Until recently, mercury high pressure bulbs dominated - there were 1241 of them, compared to 743 sodium ones and 546 LED lamps. After the reconstruction of public lighting, all light bulbs are LED. The paper compares the consumption of electricity for the four months of 2019 and the same four months of 2020. It is shown that electricity consumption for these four months was reduced by an average of 55%, the average reduction in CO2 emissions was 55%, while the reduction in electricity costs was an average of 58%.

Fatigue State Detection for Tired Persons in Presence of Driving Periods

Because of the increased number of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this potentially dangerous condition. The developed algorithms’ main problem is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data are gathered from the deriver’s brain Electroencephalogram (EEG) signals or video streaming recordings of the driver’s behavior. This paper proposes two distinct approaches to producing a high-performance fatigue detection system, the first based on the use of machine learning classifiers and the second depending on the use of deep learning models. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolutional Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time (TT) when compared to all other recent and efficient fatigue detection algorithms.