Impact Velocity Prediction in a Traffic Accident

Developed countries, especially ones with good geographic and climatic characteristics for cycling, recognized long time ago the benefits of using bicycle as a mean of transport, peculiarly by motivating passenger car users towards the wider use of bicycle. Some of the most significant advantages of cycling are health improving, emission and congestion reductions, mitigation of problems with traffic noise and parking in central urban areas etc. Traffic casualties are global world problem and cyclists are especially demoralized for resolving the above mentioned problems because they are classified as vulnerable road users. According to that, it is essential to take measures for improving the level of cyclists safety in traffic, and thus to create conditions for popularization of cycling. Besides phenomenological approach in traffic accident investigation and reconstruction (spatial distribution, time distribution, types of accidents, particular characteristics of traffic accidents, etc.), etiological approach is more significant because of more detailed causes and circumstances analyses of cyclists endangerment in traffic and results of these analyses are basis for measures in traffic safety improvement. Traffic accident reconstruction is the analysis of the occurring circumstances, but also analysis of circumstances under which could be avoided. Expertise presents investigation which is done by expert, so in traffic accident reconstruction it is necessary to realize a lot of complex procedures in order to assess traffic accident occurrence. Part of a time-distance analysis which is the most relevant for defining the causes and circumstances of traffic accidents is avoidance analysis.

Proper evaluation of ski helmet designs and safety standards should rely on head impact conditions involved in skiing and snowboarding head injuries. To study these impacts, main crash scenarios involving head injuries are numerically replicated. Multibody models of skiers and snowboarders were developed to investigate five common crash scenarios involved in traumatic brain injury: forward and sideways skiing falls, snowboarding backward falls, collisions between users and collisions with obstacles. For each scenario, the influence of crash conditions on head impact (location, speed, linear and rotational accelerations) and risk of injury are evaluated. Crash conditions were initial velocity, user height, position and approach angle, slope steepness, obstacles, and snow stiffness. One thousand one hundred forty-nine crashes were simulated and three significant levels of impact conditions were discriminated over the investigated crash scenarios: 1) the smallest normal-to-slope impact velocities (6 km·h; 22 km·h) and peak linear accelerations (42g; 75g) were obtained during forward and sideways skiing falls; 2) snowboarding backward falls and collisions between users were associated with high normal-to-surface impact velocities (26 km·h; 32 km·h) and head accelerations (80g; 149g) above one published threshold for mild traumatic brain injury but below the pass/fail criteria of helmet standard tests; 3) collisions with obstacles were associated with high normal-to-surface impact velocities (19 km·h; 35 km·h) and the highest head accelerations (626g; 1885g). Current impact conditions of helmet standard evaluations consistently replicate collisions with obstacles, but need to be revised to better reflect other significant crash scenarios leading to traumatic brain injury.

SINRAT is a computer program for reconstruction of traffic accidents, one that constitutes a powerful tool in the calculation applicable to the investigation, reconstruction and analysis of the damage produced in traffic accidents. SINRAT is provided with an input data pre–processor that allows the introduction of all the factors involved in an accident like braking, acceleration and steering manoeuvres executed by drivers, road profile, etc. SINRAT includes a database where the technical features of about 7000 vehicles are found, so that when it is necessary to reconstruct an accident, you just have to indicate the make and model of the implicated vehicles. SINRAT incorporates a simulation model of the behaviour of vehicles using the bond graph technique. Each vehicle is considered as a three–dimensional body, linked to the wheels through the suspension system. The tyre–road contact is also modelled. The collision model used permits simulation of the behaviour of the vehicles before, during and after the collision. This collision takes into account also the stiffness of the car's body and its energy absorption during the collision. SINRAT incorporates a driver model that determines the steering manoeuvre so that the vehicle traces a prearranged trajectory. It incorporates also the possibility to introduce the real profile of the road when it refers to the raisings, ramps, transition curves, etc. Even more, it includes an algorithm for the automatic reconstruction of traffic accidents, which permits calculation of vehicle velocity from the data at the scene of the accident. Starting from these data, the program reconstructs the accident varying the collision velocities until it reproduces the point of collision as well as the final positions of all vehicles. Lastly, SINRAT possesses a graphic post–processor which permits the visualisation of the movements of the vehicles and the visualisation of variables in terms of x–y plots.

BackgroundThrombolysis and mechanical thrombectomy represent the most successful stroke innovations over the last 30 years. Quantifying innovation in stroke is essential for identifying productive research lines and prioritizing funding, but health care lacks validated methods for measuring innovation.ObjectiveThis study aimed to systematically evaluate the relationship between stroke-related patents and publications, demonstrate the feasibility of using large language models (LLMs) in this process, and identify the most rapidly advancing innovations in stroke care by mapping them to a theoretical innovation life cycle.MethodsThe Open Patent Services (European Patent Office) and PubMed databases were searched between 1993 and 2023 for “stroke OR cerebrovascular.” In this bibliometric patent-publication analysis, a 13 billion–parameter Llama LLM was trained to identify patents related to stroke disease, as opposed to other references to the word “stroke,” on a manually labeled subset of 5000 patents and assessed using 5-fold cross-validation. The LLM filtered irrelevant results, and the resulting patent codes were grouped into innovation clusters. For each cluster, annual patent and publication counts were normalized to adjust for global trends. Cluster-specific growth curves were plotted to analyze the rates and characteristics of growth. The innovation life cycle stage for each innovation cluster was estimated by fitting a sigmoid curve to the patent and publication data consistent with the diffusion of innovations theory by Rogers.ResultsThe cross-validated accuracy of the LLM was 99.2%, with a sensitivity of 96.5% and a specificity of 99.6%. An initial bibliometric search retrieved 237,035 patents and 486,664 research publications. A manual review of a random sample of patents before filtering revealed that only 11.2% (56/500) were relevant to stroke. After LLM filtering, of the 237,035 patents, 28,225 (11.9%) stroke-related patents remained. These were grouped into 7 innovation clusters: pharmacological treatment, alternative medicine, rehabilitation devices, medical imaging, diagnostic testing, surgical devices, and artificial intelligence (AI) methods. Patent and publication counts were strongly correlated across clusters (Spearman rs=0.65-0.92; P<.006) except for pharmacological treatment (rs=0.09) and alternative medicine (rs=0.55). Pharmacological treatments were the top-performing cluster over the last 30 years, accounting for 49.3% (36,005/73,094) of all patents, but patent activity in this area has plateaued since the late 2000s. AI methods, rehabilitation devices, and medical imaging exhibited exponential rates of patent growth, with annual normalized increases of 39.2%, 15.9%, and 5.8% compared to 16.9%, 5.3%, and 2.2% for publications, respectively.ConclusionsApplying an LLM to publicly available patent and publication data provides a scalable way to quantify innovation in stroke. Pharmacological treatment appears to have entered a saturation phase, whereas AI methods, rehabilitation devices, and medical imaging remain in rapid growth, highlighting areas of greatest traction for future research and investment.

Traffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010–2017 in Sarıyer district of Istanbul, Turkey’s largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran’s I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.

The objective of traffic accident reconstruction is to recreate the event, which is necessary for analyzing the collision dynamics that is used as evidence in court cases. Traffic accident reconstruction and a demonstration of the event require precise data pertaining to scene measurement. However, there are differences between the individual measuring tools and methods related to traffic accident investigation, just as there are differences between the extent of their use and measurement accuracy. The most commonly applied method is the measuring tape, followed by measurements with total stations and laser rangefinders, while photogrammetry is also becoming increasingly important. The advantages and disadvantages of individual tools and methods affect the required number of investigators, portability, measurement range, applicability depending on the amount of light and weather conditions, on the possibility of remote measurement, on data collection time, on the scope, on the option to later process, the collected data and above all on the accuracy of all gathered data. The latter is crucial for proving the guilt or innocence of traffic accident participants at court, as inaccurate data can lead to an unjust sentence. Measurement accuracy using the above mentioned tools and methods also varies depending on which ones are used, as well as on other factors.

The safety of road traffic is facing increasing challenges from a range of factors, and this study aims to address this issue. The paper describes the development of a model that assesses both the quantitative and qualitative aspects of the current traffic situation and can also predict future trends based on monthly data on traffic accidents over a period of years. The dataset is composed of the number of accidents that occurred in the Pristina region over a 10-year period, and these are categorized based on the type of accident and safety factors, including human, vehicle, and road factors. By using machine learning algorithms, a model has been developed that determines the factor with the greatest impact on traffic safety. To create the model, the algorithms Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Random Trees (RT) were used. The model evaluates the contribution of human, road, and vehicle factors to traffic accidents, using machine learning algorithms and 36 types of traffic accidents to analyze the relevant statistics. The results indicate a very good fit of the model according to the MLR algorithm, and this model also identifies the road factor as the main influencer of the traffic safety level. Doi: 10.28991/CEJ-2024-010-09-06 Full Text: PDF

Accurate and detailed information of traffic accident scene is important for accident investigation, and the current investigation methods (tape measuring and total station survey) always need highway closure, and their working process is time-consuming. From different angles or at different altitudes, unmanned aerial vehicle (UAV) can monitor accident site without interrupting the traffic flow, therefore, UAV is introduced for accident scene reconstruction. Firstly, the method framework of accident scene reconstruction was proposed, in which UAV was used to take pictures of accident site, and imaging system was adopted to reconstruct the 2D and 3D accident scene. Then, 3D reconstruction, point cloud generation, and model optimization were presented. Next, a UAV flight experiment was conducted for traffic accident scene reconstruction, and two evaluation indexes, signal-to-noise ratio and structural similarity, were introduced to assess the image quality of accident scene reconstruction. The case study demonstrates that compared with current methods, the proposed method is efficient; moreover, the effect of accident scene reconstruction is satisfactory.

Accident rescue is an important topic for life saving and emergent handling. One issue of accident rescue is how to shorten the times for emergency vehicles (such as ambulances, fire engines, and police cars) to arrive at accident scenes so that the emergent events can be properly processed. For example, an ambulance should arrive at a traffic accident scene as fast as it can so that the wounded can be taken care of as soon as possible. However, in metropolitan areas traffic jams may make emergency vehicles difficult to arrive at the accident scenes. This is because cars on the roads may not have enough space to move themselves to empty a lane for the pass-through of emergency vehicles. In this condition, the valuable time for rescuing injured people or extinguishing fire will be wasted due to undesired traffic jams, which may result in unrecoverable tragedies. To solve this problem, in this thesis we propose a traffic control mechanism based on the IEEE 802.11p/1609 radio technology to control the traffic condition and traffic lights to shorten the time required by emergency vehicles to arrive at accident scenes. In the proposed mechanism, a traffic control center is deployed to monitor the traffic conditions on the roads and provide the traffic information for emergency vehicles. The traffic control center can dynamically adjust the traffic lights on the roads to help emergency vehicles arrive at accident scenes more quickly. Our simulation results show that the proposed mechanism can significantly reduce 30% to 50% arrival times required by emergency vehicles to arrive at accident scenes in our evaluated cases.

Traffic accident reconstruction is a reverse dynamic problem, which requires hundreds of iterations to reconstruct the whole process of accident. However, in current pedestrian-vehicle accident reconstructions, it is difficult to quickly establish a pedestrian model based on specific cases, and it is hard to solve the contradiction between calculation accuracy and calculation time. In this paper, a personalized pedestrian customization method is proposed. First, the pedestrian structure is divided into independent modules according to obvious bony markers. For each independent module, multi-body (MB) model and finite element (FE) model are established, respectively. Then the appropriate modules are selected to form the whole hybrid pedestrian model. This method can customize the structure of pedestrian model according to the injury characteristics of pedestrians in specific accidents, and customize the parameters of pedestrian model according to the height and weight of pedestrians. The impact simulation tests are carried out on hybrid pedestrian models to verify the reliability of the models. The proposed method can effectively improve the modeling efficiency of pedestrian models and the reconstruction quality of pedestrian traffic accidents.

Posttraumatic stress disorder (PTSD) is possibly an overlooked diagnosis of victims suffering from traffic accidents sustaining serious to severe injuries. This paper investigates the incidence of PTSD after traffic accidents in Germany. Data from an accident research unit were analyzed in regard to collision details, and preclinical and clinical data. Preclinical data included details on crash circumstances and estimated injury severity as well as data on victims' conditions (e.g. heart rate, blood pressure, consciousness, breath rate). Clinical data included initial assessment in the emergency department, radiographic diagnoses, and basic life parameters comparable to the preclinical data as well as follow-up data on the daily ward. Data were collected in the German-In-Depth Accident Research study, and included gender, type of accident (e.g. type of vehicle, road conditions, rural or urban area), mental disorder, and AIS (Abbreviated Injury Scale) head score. AIS represent a scoring system to measure the injury severity of traffic accident victims. A total 258 out of 32807 data sets were included in this analysis. Data on accident and victims was collected on scene by specialized teams following established algorithms. Besides higher AIS Head scores for male motorcyclists compared to all other subgroups, no significant correlation was found between the mean maximum AIS score and the occurrence of PTSD. Furthermore, there was no correlation between higher AIS head scores, gender, or involvement in road traffic accidents and PTSD. In our study the overall incidence of PTSD after road traffic accidents was very low (0.78% in a total of 32.807 collected data sets) when compared to other published studies. The reason for this very low incidence of PTSD in our patient sample could be seen in an underestimation of the psychophysiological impact of traffic accidents on patients. Patients suffering from direct experiences of traumatic events such as a traffic accident and presenting with signs of clinically significant distress or impairment in social interactions should be treated in a team approach including not only trauma surgeons and surgical skilled staff but also psychophysiological experienced physicians.

Pedestrians often suffer severe injuries in road traffic accidents. Therefore, pedestrian safety and, more precisely, analysis of injuries of the knee joint and lower limb bones were the focus of this research. A Vietnamese-sized human body model (HBM), named V-THUMS, scaled from the Total Human Model for Safety (THUMS) representing a walking pedestrian, was utilized in a numerical simulation. A pickup truck model was used to simulate pedestrian collisions with impact speeds from 20 km/h to 50 km/h. To reduce the computational time, the pickup truck model was simplified. In addition, the collision angle was varied from 0o, 30o, 60o, 90o, -30o, -60o to -90o to explore the effect of angle position on the injury mechanisms of the leg. The results illustrate that in some cases at low speeds there is a relationship between bone fracture and ligament rupture that does not exist at high speeds. The femur is very difficult to fracture because the bone is very hard and thick. It was found that at 20 km/h impact speed, the lower limbs are unharmed.

A retrospective cross-sectional study of injury patients who attended the Accident and Emergency Unit was conducted at the University of Malaya Medical Centre. Demographic data with regards to age, sex, race, occupation and the relevant data such as type of accident, type of injury sustained and also severity seen during the month of January 2005 were selected retrospectively from medical record. The objectives were to review road traffic accidents and their severity of injuries among the patients. There were two hundred and forty-three (197 males and 46 females) patients involved: 60% Malays, 16% Chinese, 21% Indians and 3% other ethnic groups. Peak accidents occurred among the 21 to 30 years age group. Among the type of accidents, 65.8% involved motorbike riders, 10.3% car drivers, 8.6% pillion riders, 7.8% car passengers, 5.8% pedestrians and 1.6% were bicycle rider injuries. About 57.6% sustained minor injuries and 42.4% major injuries. There was no significant association between outcome of road traffic accidents and sex, age group and race. However, there was a significant association between type of accidents and severity of injuries (p = 0.014). Males sustained more major injuries and motorbike accidents were the highest among road injuries. Therefore, further prevention and control with emphasis on behavioural changes, education and law enforcement may reduce the number of road traffic accidents in the future.

With respect to the restricted diagnostic possibilities at an accident scene and the limited time for decisions essential for survival, this project dealt with severe injuries with an abbreviated injury scale (AIS) 3-6 in traffic accidents. The objective was the development of a diagnostic aid that can be used directly at the accident site. The analysis was based on data from the GIDAS (German in depth accident study) files from the years 1999-2006. To investigate the accident parameters and to draw up the injury risk functions a multivariate regression analysis of 5,010 traffic accidents was performed. For the 20 most prevalent severe injuries of passenger car drivers and front seat passengers the cumulative influence of clearly evident factors was depicted and the combined likelihood was calculated. For the utilization at the accident scene software has been developed to display these likelihoods in a simple and comprehensible manner. At the accident scene crucial diagnostic indications can be acquired with simple observation and a software-assisted analysis.

After the traffic accident, it is a very important work on how to collect evidence from the scene, draw accident scene map, and clean up the area as soon as possible, without causing further casualties. In this paper, a method of drawing traffic accident scene map using small Unmanned Aerial Vehicles (SUAV) image mosaic based on rectification is proposed. Firstly, the accident scene images collected by SUAV are preprocessed and rectified through plane homograph. Then, two images are stitched into a large view image through feature point matching and image fusion which contains Harris corner detection and SIFT feature matching algorithm. Finally, taken the accident mosaic image as background, the accident scene map is drawn. Using this method, the scene information can be displayed correctly, which is convenient for the traffic accident scene investigators to draw the accident scene map, and ensure the complete panoramic information collection of the whole scene.