Driver Workload Characteristics Analysis Using EEG Data From an Urban Road

Dear Editor-in-ChiefNurses spend much more time dealing with patients compared with other healthcare personnel. This group has a significant role in identification of threatening complexities of patients, removing medical errors and in keeping patients safe, on the other hand, a considerable amount of human errors is committed by nurses (1). Therefore, nurses have to face with many occupational risks compared with other jobs and may suits relating to occupational negligence (1). Human errors associated with nursing duties; in particular, medication error has been identified as a central concern for the nursing profession (1). As the result of such events, patient is hurt unwantedly and staff suffers a great deal of damage. These events often result from the change of systemic specifications in duties, group, work environment, and organization (3).The present study was a cross sectional research aimed at comparing two human error identification and assessment methods, SPAR-H and CREAM, in nursing practice at a hospital in Iran. We investigated 31 duties according to job description announced by Iran Nursing Organization and professors of nursing.In the first step, we analyzed occupational duties using Hierarchical Task Analysis (HTA) method (4). Next, different tasks are executed for each sub task. After completing CREAM and SPAR-H worksheets, we first grouped the worksheets for each duty and analyzed total error probability and error risk using SPSS-16 (Chicago, IL, USA). In order to compare the two techniques based on multivariable decision making, we used analytical hierarchy process (5).The findings of demonstrated that, the estimated human error probability by SPAR-H was higher than those estimated by CREAM, but the most critical task, taking initial measures for cardiopulmonary resuscitation until the arrival of resuscitation team, was the same in both techniques. Moreover, it was determined that both techniques have similar results in identifying and assessing critical tasks with high probability of human error (Table 1). Furthermore, there was more difference between these two techniques in ranking potential errors with low probabilities; for instance, preparing bed before arrival of patient have the rank of 27 when we used SPAR-H technique 27, while its rank using CREAM technique was determined at 10 (Table 1).Based on previous studies in this field and also eliciting domain expert knowledge, seven criteria were selected for comparing two techniques with each other (6). …

Given the high incidence of traffic accidents and fatalities on two-wheeled vehicles, there is a growing need for safety management. However, studies on evaluating two-wheeled vehicle driving in a quantitative and comprehensive form are insufficient. In this study, 11 items were defined for the first step to evaluate two-wheeled vehicle driving: signal violation, central line violation, helmet violation, pedestrian close driving, sidewalk driving, reverse lane driving, speed violation, rapid acceleration, rapid deceleration, rapid turn, and rapid lane change. The items were classified into three categories (traffic violation, pedestrian threat, and reckless driving), and their weights were derived using the AHP technique. For rapid acceleration, rapid deceleration, rapid turn, and rapid lane change, a high-performance driving simulator was used to establish risk criteria and calculate the weight based on the degree of risk. The calculated weight of each item indicates its importance in evaluating two-wheeled vehicle driving, with helmet violation (0.158), speed violation (0.124), and pedestrian close driving (0.122) having the highest weights. Finally, the dangerous driving index for two-wheeled vehicles was calculated by the weights of each evaluation item and applied to the driving trajectory data.

In today’s world, as the technology is increasing day by day, road accidents are becoming more and more prevalent. Driver error is the most common cause of traffic accidents, and with cell phones, in-car entertainment systems, more traffic and more complicated road systems, it isn't likely to go away. To reduce the human error, there arises a possibility if with use of technology this error can be reduced and driving can be safer. Driverless cars are one of the major topics where transportation and innovation are considered to be perfectly combined for the safety on roads. Every automotive player is trying its best to make it practically possible in whichever it could. Companies developing and/or testing driverless cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen and Volvo. One of the major ongoing projects is “Waymo” by Google. In this project we designed a prototype of driverless car with basic features using Arduino uno. We have used gmaps to create a map where we used to give the source and destination to get the route then we download the route in Keyhole Markup Language (KML) file format then we go to convertcsv.com site to extract latitude and longitude from the KML file that we just downloaded from the gmaps. We copy the latitude and longitude to our Arduino uno code where we keep the latitudes and longitudes in form of an array. Then we have built an app which uses the mobile Assisted Global Positioning System (AGPS) sensor to sense the current real time position in terms of latitudes and longitudes and send them to things peak cloud and simultaneously displaying them on a real time map. Arduino uno then receives the latest data sent from the mobile to the cloud and to do that Arduino uno uses a Wi-Fi module (esp 8266) to connect to the internet. Then the thingspeak cloud send the data receive from the mobile to the Arduino uno and then using that data we calculate the degree and distance required to move the car according to the latitudes and longitudes stored in the array which we got from the gmap route.

The high impact of traffic accidents makes it imperative to formulate public policies to reduce their occurrence. In this task, knowing the cause of accidents is of paramount importance. The use of data mining and big data adapts to the complexity of the phenomenon under study. In order to classify some possible causes of traffic accidents, we built a data model to describe the behavior and dynamic of the participant agents in the traffic accident event in Colombia. This paper presents the application of MLP and Naive Bayes algorithms to identify the possible immediate cause and the rules decision algorithm PART for the root cause of traffic accidents. Models have been tested aiming to obtain the goodness of fit by increasing metrics like Recall, Precision, ROC and Kappa index, and minimize the RMSE.

Review of “Just culture – Balancing safety and accountability” and “The field guide to understanding human error”

In this paper a new type of GIS system for maritime navigation safety is proposed. The system takes advantage of the properties of the kinetic Voronoi diagram, and uses the quad-edge data structure for the real-time maintenance of the spatial relationships of ships and other navigational objects. The locations of ships are updated using a standard onboard AIS transponder, and moving-points Voronoi algorithms. The spatial relationships are used for collision detection and avoidance. The system is aimed at tackling the main cause of marine accidents - human errors - by providing navigational aid and decision support to navigators. The brief introduction into static and kinematic Voronoi methods is given and is followed by the discussion of different types of GIS systems for marine purposes. The state-of-the-art in maritime safety and its major developments are also covered.

In this paper a new type of GIS system for maritime navigation safety is proposed. The system takes advantage of the properties of the kinetic Voronoi diagram (VD), and uses the quad-edge data structure for the maintenance of the spatial relationships of ships and other navigational objects. The locations of ships are updated in real-time using a standard onboard transponder, and moving-points VD algorithms. These spatial relationships are used for collision detection and avoidance. The system is aimed at tackling the main cause of marine accidents - human errors - by providing navigational aid and decision support to mariners. A brief introduction into static and kinematic Voronoi methods is given and is followed by a discussion of different types of GIS systems for marine purposes. The state-of-the-art in maritime safety and its major developments are also covered.

In this study, statistical features of traffic accidents in Northern Cyprus (TRNC) especially for the last decade were examined. Such that, risk factors (causes) of traffic accident and their demographic reflections were identified by evaluating official statistics. The main findings of this study are these: Fast driving as the most important cause of traffic accidents; Straight road as the chief location of severe traffic accidents; Inverse relationship between the severe traffic accidents and driver's licenses period (experience); and Inverse relationship between the severe traffic accidents and the age of accused persons.

Using system simulation to model the impact of human error in a maritime system

Accidents provide important learning points to prevent future mishaps. In its approach to accident prevention one technique that Shell uses is the Tripod accident analysis tree.In the past there has been a tendency in all organisations to blame accidents on the people who suffer them and to see unsafe acts as arising from the stupidity, carelessness or recklessness of particular individuals. Accident investigations frequently cite 'human error' as the cause and many people still see such errors as the beginning and end of the accident sequence. The three more tangible components of such accident investigations are unsafe acts, failures of defences and the accidents themselves. Such unsafe acts or 'active failures' are often seen as the most important part of the accident story. In reality the story has its beginnings much earlier in areas such as Design, Procedures, Training or Maintenance Management, areas known as 'latent failures',To better understand the underlying causes of accidents, in 1988 Shell commissioned a behavioural research program called Tripod. Tripod does not address unsafe acts and hazards directly, but places them in context in a model of an accident which seeks to define the underlying organisational factors that provoke unsafe acts, and that allow errors to turn into accidents. The research has established that latent failures may be categorised into a small number of disruptive processes. These latent failures are incorporated into an accident diagram in the form of a 'tree', and improve our understanding of the causes of accidents, and direct our recommendations for improvement into areas that are likely to be the most effective.

It is well known that human error is the key contributor in most marine accidents. Nevertheless, human error is not the cause of accidents or mishaps in the new view on human errors; it is the deeper trouble of the MMEM (Man-Machine-Environment-Management) system. The key point for human error analysis is to find out the connection between human error and the features of system elements, that is the contexts of human error (Operating environment, Organization influence, etc). For this reason, this paper shows an approach for mining the human factors data which influence the occurrence of human error. This approach bases on the data collected on HFACS (Human Factor Analysis and Classification System) Model. It can be used for analysis of human factors and risk management of marine industry.

Received: May 2013, Accepted: December 2013 Background: Human error is the most important cause of occupational and non-occupational accidents. Because, it seems necessary to identify, predict and analyze human errors, and also offer appropriate control strategies to reduce errors which cause adverse consequences, the present study was carried out with the aim of identifying human errors while operating meat grinder and offer suggestions in order to reduce human errors in this human-machine system. Materials and Methods: This is a descriptive study. In this ergonomic study the “Task Analysis for Error Identification (TAFEI)” technique was used in order to identify human errors while operating a meat grinder machine. According to this technique, firstly, tasks of human side of the interaction were described by Hierarchical Task Analysis (HTA) and then the State-Space Diagrams (SSDs) were drawn. Finally, after forming the TAFEI diagrams, the transition matrix table was prepared in order to identify human errors. Results: After completing all the steps of TAFEI technique, the transition matrix table was formed. Results showed 49 illegal transition states; therefore, 49 human errors were identified and described while operating the meat grinder machine. Conclusion: The results of this study showed how and under which conditions may meat grinder users do error in the human-machine interaction. In this regard, possible human errors resulting from non-ergonomic design of Iranian meat grinder machine were identified.

Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.

The number of causalities, injuries, loss of life and properties accompanied with marine pollutions per year recorded from marine accident is quite alarming in Niger Delta. A very large proportion of these accidents have been attributed to human errors. This study elucidates the main human factors or errors that have contributed to the incidence of marine accident in the Niger Delta navigational waters. A descriptive and quantitative survey was carried out and data collected using well-structured questionnaires distributed to 400 respondents, out of which 287(71.8%) was retrieved. An appropriate statistical tool (SPSS Softwares-2010 Chicago version) such as multiple logistic regressions using The Adjusted Odd Ratio (AOR) was employed for predictors/independent variables at 95% confidence interval. The socio- demographic results showed that majority (78.4%) of the respondents are of sound mind with over 10 years of working experience. The P-value is 0.02 for the multiple logistic regressions; there exist a statistically significant relationship within the predictors (independent variables) and incidence of marine accident in the Niger Delta. The constituents of Human errors do not contribute equally towards marine accident incidence. Six(6) out of the ten(10) identified human related factors/error namely crew fatigue, unruly behaviour, unsafe vessel speed, commercial pressure from management, lack of maintenance culture, organisational structure/inadequate safety culture were frequent human causes of marine accident and are significantly related to safety performance. The highest ranking human error/factor is unsafe vessel speed of about 3.01 times higher odds of occurrence of marine accident while the least human factor is the influence of drugs and alcoholism with just 1.01 times higher odds. It is advisable for mariners to have positive and robust safety culture in place. Employers should recognise, encourage and reward Staff with impressive s

A FATAL MOTOR-CAR ACCIDENT AND CANNABIS USE: Investigation by Radioimmunoassay