Accident Categories Research Articles

Comparison of GAP, R-GAP, and new trauma score (NTS) systems in predicting mortality of traffic accidents that injure hospitals at Mashhad University of medical sciences

BackgroundThere are several trauma scoring systems with varying levels of accuracy and reliability that have been developed to predict and classify mortality in trauma patients in the hospital admission. Considering the importance of the country's emergency organization and the World Health Organization in the category of traffic accidents, we used this information in the study. The objective of this study is to evaluate and compare the predictive power of three scoring systems (R-GAP, GAP, and NTS) on traffic accident injuries. MethodsIn an analytical cross-sectional study, all the data related to the mission of traffic accidents at the pre-hospital emergency management of Mashhad University of Medical Sciences in 2022 were extracted from the automation system, and the outcome of the patients in the hospital was recorded from the integrated hospital system. Then, GAP, R-GAP, and New Trauma Scores (NTS) were calculated, and their results were compared using ROC curve and logistic regression. ResultsIn this study, 47,971 injuries from traffic accidents were evaluated. Their average age was 30.16 ± 10.93 years. R-GAP showed negligible difference than GAP and NTS scores (the area under the curve equals 0.904, 0.935, and 0.884, respectively), and the average scores of R-GAP, GAP, and NTS are equal to 22.45/45 ± 1/9, 22.25 ± 1.5, and 22.49 ± 1.3, respectively. Injury severity based on R-GAP, GAP, and NTS scores was mild in most patients. The effect of these models on the patient outcome based on OR values, R-GAP, GAP, and NTS models showed high values. All analysis was performed in SPSS 26. ConclusionAccording to the study results, it seems that R-GAP, GAP, and NTS, have the highest power to predict death in traffic accident injuries. It is recommended to include these points in the electronic file of the pre-hospital emergency for the injured. Also, the severity and outcome of the patient can be predicted by these scores, which play an important role in the triage of the injured and determining the appropriate treatment center.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Predicting maritime accident risk using Automated Machine Learning

Machine learning (ML), particularly, Automated machine learning (AutoML) offers a range of possibilities for analysing large volumes of historical maritime accidents data with advanced algorithms for integrating predictive analytics in operational and policy decision-making for improving maritime safety. This study explores historical data of maritime accidents in Norwegian waters over 40 years. The data has been utilised for analysing five major maritime accident categories: grounding, contact damage, fire or explosion, collision, and heavy weather damage. A total of 29 classification ML algorithms were trained, and the Light Gradient Boosted Trees Classifier was found to be the best-performing with the highest predictive accuracy. The three most impactful factors for accident risk are the category of navigation waters, phase of operation, and gross tonnage of the vessel. Based on the feature effect results, vessels sailing in narrow coastal waters, in the along-the-way operational phase, and fishing vessels are highly vulnerable to grounding relative to other types of accidents. The results can be used as input for the entire procedure of risk analysis, from hazard identification to quantification of accident consequences, and the best-performing ML algorithm can be utilized in developing a decision support system for real-time maritime accident risk assessment.

Implementation Of Fuzzy Logic to Identify Accident Categories In SMS-Based Two-Wheeled Vehicles

Two-wheeled vehicle is the most popular means of transportation in Indonesia. As a result, it would cause several issues. One of them is an increased possibility of an accident to happen. In the event of an accident, quick aid from the public to the victim can reduce the risk of severe injury suffered by him. The person who provides aid to the injured victim may ask for money from the victim's family whose amount is not proportionate with the severity of the accident. In light of this, a system has been devised to identify and categorize the different types of accidents and send the formation to a family phone number registered electronically. The accident level is categorized using the Sugeno-type fuzzy logic method. The parameters used to differentiate the accident categories are speed, slope, and duration of vehicle braking time. The information is then sent to the registered phone via SMS that contains the accident category and the coordinates of the accident location provided by the GPS Neo 6 module. The algorithm is based on the vehicle's tilt angles, which range from 45(to the right) and -45(to the left). The fuzzy logic then determines the category, which processes and produces the accident category based on the speed and vehicle braking duration parameters. The proposed algorithm in this research will be experimented with using a real motorbike. Based on the experimental results, it has been found that the performance of the fuzzy logic method has an accuracy of 88.89% when determining the category of accident (light or heavy) and the time taken to send the information to the family member via SMS is quite fast.

Road accidents on Indian National highways, ambulance reachability and transportation of injured to trauma facility: Survey-based introspection of golden hour.

The transportation system plays a crucial role in the context of socioeconomic development, whereas the highway infrastructure acts as a base for the transportation system. In recent years, a rich impetus has been given to the development of road infrastructure by Indian governance. There is a need to introspect how well the prevailing highway infrastructure is equipped with emergency rescue management during road accidents. Lack of ambulance service and trauma facilities along the highways results in a steady loss of lives and injuries and increases people's exposure to risks. This study aims to determine the response time of ambulance reachability to the accident spot on Indian national highways associated with heavy commercial transportation. Also, determining the time to transport the injured to the nearest trauma facility is another factor included as an objective in this investigation. The study adopted survey-based research, whereby the variables in the questionnaire were designed to record and assess the time for an ambulance to reach the accident spot and, from there, to transport the injured to the trauma management facility on Indian highways. Two hundred twenty-five participants who were either victims/relatives of victims or those involved in the rescue of the injured have participated in the survey. The dates of the accident events were 2017 and 2022. The survey resulted in the identification of two categories of highway accidents. The first category of accidents happened on the highways near city limits/dense settlements, and the second category occurred on the core highways. The percentage of accidents caused on the highways either adjacent to or passing through the city limits/dense settlements was reported to be higher than the accidents on the core highways. Ninety percent of the participants reported successful contact with the ambulance call/service centre, but only ~75% success rate exists for ambulances to reach the accident scene. On the core highways, the time taken for the ambulance to arrive at the accident scene is 25-35 minutes. The results from the survey ascertained that the patients were prioritised for treatment in the nearest hospitals (irrespective of having a trauma facility) at a distance of ~12-20 km, for which the time taken is ~15-25 minutes. Importantly, from the interviews, it is understood that in many cases, these hospitals have further referred to specialty hospitals located in nearby cities or trauma centres with greater facilities. Occasions exist where the injured were taken directly to hospitals 30-40 km from the accident spot, for which the time was more than 40 minutes. The results provide evidence that in either of the accident cases on the highways that are adjacent to/passing through the city limits or on the core highways, the total time for emergency care accessibility is nearly 60 minutes or greater; this implies that in the majority of cases, there is very meagre time left to provide emergency medical care to the needy and injured on the Indian highways to abide by the concept of golden hour. Plausible reforms backed by technology for enabling highways into 'emergency rescuable highways' are highly needed to guarantee a safer and more sustainable transportation system in India.

Harnessing sensor fusion and AI for accurate accident detection and classification in the safety of smart cities

With the growing number of automobiles, traffic accidents are increasing daily. The World Health Organization (WHO) study reports that annually, 1.4 million individuals have died, and 50 million have been wounded globally. An advanced accident detection technique using cognitive agents will reduce rescue operational delays, perhaps saving several lives. Intelligent Transportation Systems (ITS) are gaining significant attention in academia and industry because of the increasing popularity of smart cities. They are seen to enhance road safety in these urban areas. Internet of Things (IoT) and Artificial Intelligence (AI) systems have been widely used to decrease the time needed for rescue operations after an accident. This study introduces an IoT-enabled Automotive Accident Detecting and Categorization (IoT-AADC) method that combines a smartphone's internal and external sensors to identify and categorize the kind of accident. This innovative method enhances the effectiveness of emergency support like fire departments, towing agencies, etc., by providing crucial information regarding the accident category for better planning and execution of rescuing and relief activities. Emergency support providers enhance their preparedness by assessing the injuries experienced by those injured and the damage to the automobiles.

ANALYSIS OF MARITIME ACCIDENTS IN MALAYSIAN WATERS

Throughout the South China Sea and the Malacca Strait, more than 60% of all maritime trade passes each year. The rapid growth in fleet size and ship size may lead to an increase in maritime accidents. Since many maritime accidents cause serious injuries, fatalities, damage of property and monetary losses, it is essential and crucial to discuss about marine safety. In this study, a statistical analysis was performed in order to assess the number of maritime accidents that occurred in Malaysia between 2018 and 2021, as well as the percentage of accidents that occurred in each accident category and for each type of ship. The analysis also considers the age of the ship. The data that were presented also looks into a potential relationship between the age of the ship and the accident percentage. The results demonstrate that general cargo ships were the ship categories that are most vulnerable to maritime accidents; collisions were the accident type that occurs most frequently, and there were several relationships between the accident percentage and ship age. The results may be utilized to help seafarers, related government agencies and other relevant organizations promote risk prevention, create efficient risk response plans, and establish strategies to enhance the marine mechanism for managing safety in Malaysian waterways.

Optimising Traffic Safety – Locating Traffic Gendarmes Based on Multi-Criteria Decision Making

Turkey’s expanding population and growing economy have resulted in a significant increase in automobile ownership, leading to a rise in traffic volume and, subsequently, an increase in the number of accidents. The increase in the number of deaths and injuries caused by traffic accidents has motivated authorities and automobile manufacturers to work together to mitigate the impact of traffic accidents. Therefore, the demand for better roads, modern technologies and higher-quality driver training is becoming increasingly urgent for traffic safety. Due to the scale of harm to the country’s economy and society caused by the material and moral losses, resulting from traffic accidents, traffic safety and management is one of the most important government initiatives. One of the responsible units of traffic safety and management in Turkey is the traffic gendarme. This study reviewed the categories of traffic accidents, the number of accidents, and the road network that occurred in a province's gendarme responsibility area in Turkey and linked them with the number of traffic gendarmes in that province. Thus, the study utilised mixed integer programming based on multi-criteria decision-making methods to identify the areas where these traffic gendarmes will be deployed according to established principles.

Analysis of Personnel's Understanding of Safety Culture in the Implementation of Safety Management System at Yogyakarta International Airport

The global spread of industries towards the era of Industry 4.0, encompassing the integration of cyber technology and automation. Safety Management Systems (SMS) are employed at each airport differently due to their distinct characteristics. Airport security and order disruption are security issues. Cases such as apron breaches, trespassing, and negligence occur. Aviation accidents pose security issues stemming from SMS implementation, operational oversight, aircraft certification, and flight health factors. There are 8 accident categories and 22 serious incidents resulting in 5 fatalities.This research employs a qualitative approach with a descriptive method. To attain answers, the researcher designed a qualitative research design. Qualitative research aims to investigate, discover, describe, and explain the quality of social influences. Data analysis is conducted through data collection, reduction, display, and conclusion drawing. Validity is ensured using source triangulation. The research findings reveal that personnel safety culture at Yogyakarta International Airport aligns with SOP. Out of 165 criteria, the achievement rate is 92.7%, indicating a positive relationship between safety culture and safety management system (KP 622/2015). The effectiveness of safety management system implementation at the airport is 72%. Strategies like Avoidance, Reduction, and Segregation are used to mitigate risks. Although risks cannot be entirely avoided or eliminated, adherence to standard operating procedures (SOP) and risk mitigation through facilities and human resources help minimize these risks.

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.

SYNERGETIC PHILOSOPHY OF HISTORY AND THE PROBLEM OF FREEDOM: METHODOLOGICAL ANALYSIS

Society is considered by the synergetic philosophy of history as a “dissipative structure”. The author analyses the mechanism of self-organisation of society – social selection – in terms of its factors such as thesaurus, detector and selector (V. Bransky). Freedom is an element of social selection. It manifests itself as a way of human existence. Freedom is directly correlated with the category of accident, but unlike it, it is only a “particular” form of necessity. In contrast to the definition of freedom as a recognised necessity, the definition of freedom as a recognised accident within the framework of regularity carries the unity of motive and action. Mastering the accident factor, a person more deeply comprehends the scope of possibilities presented by a complex system of relationships and patterns, changes his/her future and the future of society.

Deep Learning and Text Mining: Classifying and Extracting Key Information from Construction Accident Narratives

Construction accidents can lead to serious consequences. To reduce the occurrence of such accidents and strengthen the execution capabilities in on-site safety management, managers must analyze accident report texts in depth and extract valuable information from them. However, accident report texts are usually presented in unstructured or semi-structured forms; analyzing these texts manually requires a lot of time and effort, it is difficult to cope with the demand of analyzing a large number of accident texts, and the quality of key information extracted manually may be poor. Therefore, this study proposes a classification method based on natural language processing (NLP) technology. First, we developed a text classification model based on a convolutional neural network (CNN) that can automatically classify accident categories based on accident text features. Next, taking the classified fall accidents as an example, we extracted key information from accident narratives using the term frequency-inverse document frequency (TF-IDF) method and presented it visually using word clouds. The results show that the overall accuracy of the CNN model reaches 84%, which is better than the other three shallow machine-learning models. Then, eight key accident areas and three accident-prone operations were identified using the TF-IDF algorithm. This study can provide important guidance for project managers and can be used for on-site safety management to help prevent production safety accidents.

A case study of unavoidable accidents of autonomous vehicles

Objective: Autonomous driving technology eliminates human errors, and thus it is a promising solution for reducing road traffic fatalities and injuries. While future autonomous driving technology may be able to reduce the number of collision accidents, it will not be able to avoid all collision accidents. This study is aimed to demonstrate why some accidents will still be unavoidable even with advanced perceiving and controlling capabilities. Methods: Because fully autonomous vehicles are currently in the laboratory stage, we used the prospective method to study the unavoidable accident of autonomous vehicles. Suitable traffic accident cases were screened from the China In-Depth Accident Study (CIDAS). Videos of the accidents were analyzed and the accidents were reconstructed using PC-Crash software. We assumed that target vehicle possesses near-perfect autonomous driving capabilities. Unavoidable accidents were determined based on vehicle dynamics and traffic constraints. The time from perceiving hazard to collision was calculated for each accident. Results: Among the 112 accidents screened, 15 cases of unavoidable accidents were identified. Three typical cases are presented in detail in this study. Based on the reasons why the target vehicles cannot avoid the collisions, we classified the unavoidable accidents into time-limit type and space-limit type. Time-limit means that vehicle cannot stop or steer out of danger in time, and space-limit means that the traffic environment does not have sufficient space for vehicle to avoid collision. Conclusions: Collision accidents will still occur even with perfect autonomous driving technology. We used the prospective method to investigate scenarios and characteristics of unavoidable accidents of autonomous vehicles. The time-limit type and the space-limit type were identified as two categories of unavoidable accidents. For the time-limit unavoidable accidents, the time from perceiving hazard to collision is typically not longer than 1.5s. The characteristics of unavoidable accidents and the estimated pre-crash warning time can provide some reference for establishing future occupant protection strategies. This study also showed the limitations of active safety and the necessity of passive safety.

Pilot performance and workload whilst using an angle of attack system

Loss of control in flight is the primary category of fatal accidents within all sectors of aviation and failure to maintain adequate airspeed – leading to a stall - is often cited as a causal factor. Stalls occur when the critical angle of the aircraft is exceeded for a given airspeed. Using airspeed as an indicator of the potential to stall is an unreliable proxy. Systems that measure the angle of attack have been routinely used by military aircraft for over 50 years however rigorous academic research with respect to their effectiveness has been limited. Using a fixed-base flight simulator fitted with a simulated, commercially available angle of attack system, 20 pilots performed normal and emergency procedures during the circuit/pattern in a light aircraft. Experimental results have shown that pilot performance was improved when angle of attack was displayed in the cockpit for normal and emergency procedures during the approach phase of flight in the pattern/circuit. In relation to pilot workload, results indicated that during the approach phase of flight, there was a moderate but tolerable increase in pilot workload. The use of such a display may assist pilots to maintain the aircraft within the optimum range and hence reduce occurrences of unstable approaches. Overall, fewer stall events were observed when angle of attack was displayed and appropriate pilot decisions made during emergencies. These results provide a new perspective on pilot workload and aviation safety.

Safety risk assessment for autonomous vehicle road testing

Objective Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments. Methods This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey. Results Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made. Conclusions The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing.

Contextual Representation in NLP to Improve Success in Accident Classification of Mine Safety Narratives

Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in the mining industry, in the current work, BERT architecture is adapted in developing the MineBERT model to accomplish the classification of accident narratives from the US Mine Safety and Health Administration (MSHA) data set. In the past multi-year research, several machine learning (ML) methods were used by authors to improve classification success rates in nine significant MSHA accident categories. Out of nine, for four major categories (“Type Groups”) and five “narrow groups”, Random Forests (RF) registered 75% and 42% classification success rates, respectively, on average, while keeping the false positives under 5%. Feature-based innovative NLP methods such as accident-specific expert choice vocabulary (ASECV) and similarity score (SS) methods were developed to improve upon the RF success rates. A combination of all these methods (“Stacked” approach) is able to slightly improve success over RF (71%) to 73.28% for the major category “Caught-in”. Homographs in narratives are identified as the major problem that was preventing further success. Their presence was creating ambiguity problems for classification algorithms. Adaptation of BERT effectively solved the problem. When compared to RF, MineBERT implementation improved success rates among major and narrow groups by 13% and 32%, respectively, while keeping the false positives under 1%, which is very significant. However, BERT implementation in the mining industry, which has unique technical aspects and jargon, brought a set of challenges in terms of preparation of data, selection of hyperparameters, and fine-tuning the model to achieve the best performance, which was met in the current research.

An Analysis of Accidents of Electrical Origin in Brazil between 2016 and 2021

Accidents caused by electricity are responsible for countless deaths and injuries of people/animals and damage to buildings and facilities worldwide. Although sufficient knowledge is available on making electrical installations safe, many countries do not have organized data on accidents of electrical origin, making it challenging to implement effective public policies to reduce the risk of accidents caused by electricity. Therefore, this research aims to present a methodology for collecting data from electrical accidents through the Google LLC alert monitor. The research methodology includes obtaining and analyzing electrical-related accident data involving electric shock, fires of electrical origin, and lightning. The application of this approach in Brazil between 2016 and 2021 validates this methodology. The analysis of this data points to a significant increase in accidents of electrical origin in the analyzed period. All categories of fatal accidents between 2016 and 2021 showed a marked increase; 10.3% due to electric shocks; 33.3%; due to lightning; 42.2% in fires caused by overload or short circuits. In the case of deaths from electric shock and lightning, more than 75% were men, and in the case of deaths from electrical fires, most were elderly and children. The analysis also includes detailed information about age and sex, the location of the accident, date, and cause. Consequently, guidelines to prevent the principal types of accidents of electrical origin from occurring could be developed.

Crashworthiness Analysis to Evaluate the Performance of TDM-Shielded Street Poles Using FEA

The mitigation of the risks of passenger injuries when a vehicle is involved in a collision with a street pole shielded with a layer of tire-derived material (TDM) was assessed. This can effectively absorb a fraction of the total energy from a speeding vehicle. Since such tests are expensive to conduct experimentally, the study relies on using the Abaqus/Explicit FEA solver to accurately calculate the non-linear nature of this scenario. Two categories of this scenario were evaluated to understand the effect a shielded street pole has on the vehicle—and the total absorbed energies during frontal and corner collisions, which are typically the most common categories of such accidents to happen. Results show that at lower speeds, these reinforcements are least effective in absorbing some of the kinetic energy applied by the vehicle, with about 5% of the energy absorbed by the reinforcement. At higher speeds, however, the results show that the TDM reinforcement absorbs about 28% of kinetic energy, which can reduce injury of the vehicle occupants, as well as decrease the damage on poles. Results for this simulation also show that there is a critical thickness of TDM that can absorb these kinetic energies, after which further thicknesses results in energies being applied back to the vehicle, therefore negating any purpose to further increase TDM thicknesses.

Effectiveness of Automatic Safety Systems in Reducing General Aviation Fatalities

General aviation accounts for 95% of fatal aviation accidents in the United States. Many of these fatalities may be preventable with the inclusion of advanced advisory or automatic aircraft safety systems; however, past research has rarely focused on the effect such systems could have if applied to general aviation. This paper addresses this gap by starting with proposed safety systems and scoring their effectiveness using detailed accident data rather than the more common approaches of analyzing accident categories or polling subject matter experts. Using a data-driven logical model, this paper evaluates four systems (terrain impact prevention, upset prevention, turbulence avoidance, and emergency landing) for potential to reduce fatalities. The scoring process appears in some detail, which starts with system requirements and National Transportation Safety Board data as inputs and results in expected fatal accident reductions. The analysis estimates that implementing all four systems may result in a 66.5% reduction in general aviation fatalities, with terrain impact prevention alone resulting in a possible 30.7% reduction. These results motivate immediate effort toward implementation and certification of aircraft safety systems into general aviation, not only to save the lives of hundreds of pilots and passengers a year, but also to prime small aircraft transportation for future developments.

Technical Failures in Helicopters: Non-Powerplant-Related Accidents

Technical failures in helicopters are a main concern for helicopter safety. The prominence of mechanical failures differs for specific helicopter operations. This analysis used 151 General Aviation accident reports from the National Transportation Safety Board online database from an 11-year time period. The information in each report was collated, including the list of findings for each accident. Possible relations between causes and specific flight operations were analyzed by looking at significant differences between expected and actual values within the dataset of categorical data. It is found that the proportion of fatal accidents in this category of accidents is low (16.6%) compared with the percentage of fatal helicopter accidents in general, as well as those of specific helicopter operations. Instructional flights appear significantly more likely to be associated with maintenance-related causes. Causes related to fatigue of aircraft parts are more often associated with ferry and positional flights, as well as helicopters with turbo-shaft engines. Future research is recommended for these specific associations to further mitigate the number of accidents with non-powerplant failures.