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Journal articles on the topic 'Car accident'
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1
Wang, Zhi, Daishi Watabe, Hideyasu Sai, Yukimichi Saito, and Masayoshi Wada. "ACDR: Autonomous-Car Drive Recorder." Journal of Robotics and Mechatronics 32, no. 3 (June 20, 2020): 634–37. http://dx.doi.org/10.20965/jrm.2020.p0634.
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Herein, a drive recorder system for autonomous cars that can be used to analyze accidents that occur during autonomous driving is proposed. In many field operational tests of autonomous cars, vehicle speed, steering angle, and camera data are recorded in the log file. Only relying on these log files without simultaneously monitoring the operator or driver’s actions results in much information being lost that can be used for accident investigation. To solve this problem, an autonomous-car drive recorder that can respond appropriately when accidents occur and minimize the possibility of accidents is proposed. In the system, during an accident, the environment detected around the vehicle and the actions of the driver and operator are recorded, and the data are used to troubleshoot the accident occurrence.
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EslamiNezhad, S. A., and M. R. Delavar. "AN INTEGRATED NETWORK-CONSTRAINED SPATIAL ANALYSIS FOR CAR ACCIDENTS: A CASE STUDY OF TEHRAN CITY, IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 335–42. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-335-2019.
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Abstract. Research on determination of spatial patterns in urban car accidents plays an important role in improving urban traffic safety. While traditional methods of spatial clustering of car accidents mostly rely on the two dimensional assumption, many spatial events defy this assumption. For instance, car accidents are constrained by the road network and rely on the one dimensional assumption of street network. The aim of this study is to detect and statistically prioritize the car accident-prone segments of an urban road network by a network-based point pattern analysis. The first step involves estimating the density of car accidents in the one dimensional space of the road network using the network kernel density estimation (NKDE) method with equal-split continuous and discontinuous kernel functions. In the second step, due to the lack of statistical prioritization of the accident-prone segments with NKDE method, the output of the NKDE method is integrated with network-constrained Getis-Ord Gi* statistics to measure and compare the accident-prone segments based on the statistical parameter of Z-Score. The integration of these two methods can improve identification of accident-prone segments which is effective in the enhancing of urban safety and sustainability. These methods were tested using the data of damage car accidents in Tehran District 3 during 2013–2017. We also performed the Network K-Function to display the significant clustering of damage car accident points in the network space at different scales. The results have demonstrated that the damage car accidents are significantly clustered.
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Sheikh, Mohammad M. R. "Optimal Scaling Categorical Principal Components Analysis: Road Traffic KSI Car Accidents in England (STATS19)." International Journal of Mathematics and Statistics Studies 11, no. 3 (March 15, 2023): 27–42. http://dx.doi.org/10.37745/ijmss.13/vol11n32742.
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Categorical principal component analysis (CATPCA) technique was applied in the road killed or seriously injured (KSI) car accidents in England based on STATS19 data so that the categorical variables of KSI car accidents can be transferred into few components with reduction of dimensionality. Finally selected 20 variables in KSI car accident database were divided to create four principal components by applying “optimal scaling CATPCA” procedure in SPSS. The statistically significant KSI car accident variables, particularly the most accountable categorical variables, were identified and quantified for developing models as well as leading to aims to reduce as well as to prevent the car accidents, particularly the KSI car accidents. It also leads to map out the possible safety improvement strategies as well as to inform the policymakers on how best to reduce the number and severity of car crashes.
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Pourroostaei Ardakani, Saeid, Xiangning Liang, Kal Tenna Mengistu, Richard Sugianto So, Xuhui Wei, Baojie He, and Ali Cheshmehzangi. "Road Car Accident Prediction Using a Machine-Learning-Enabled Data Analysis." Sustainability 15, no. 7 (March 29, 2023): 5939. http://dx.doi.org/10.3390/su15075939.
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Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.
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Elhag, Salma M., Ghadi H. Shaheen, and Fatmah H. Alahmadi. "Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services." International Journal of Information Technology and Computer Science 15, no. 6 (December 8, 2023): 1–14. http://dx.doi.org/10.5815/ijitcs.2023.06.01.
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One of the main reasons for mortality among people is traffic accidents. The percentage of traffic accidents in the world has increased to become the third in the expected causes of death in 2020. In Saudi Arabia, there are more than 460,000 car accidents every year. The number of car accidents in Saudi Arabia is rising, especially during busy periods such as Ramadan and the Hajj season. The Saudi Arabia’s government is making the required efforts to lower the nations of car accident rate. This paper suggests a business process improvement for car accident reports handled by Najm in accordance with the Saudi Vision 2030. According to drone success in many fields (e.g., entertainment, monitoring, and photography), the paper proposes using drones to respond to accident reports, which will help to expedite the process and minimize turnaround time. In addition, the drone provides quick accident response and recording scenes with accurate results. The Business Process Management (BPM) methodology is followed in this proposal. The model was validated by comparing before and after simulation results which shows a significant impact on performance about 40% regarding turnaround time. Therefore, using drones can enhance the process of accident response with Najm in Saudi Arabia.
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Farooq, Danish, and Janos Juhasz. "Simulation Analysis of Contributing Factors to Rider Visibility Issues for Car-Motorcycle Accidents." Periodica Polytechnica Transportation Engineering 48, no. 3 (April 16, 2019): 203–9. http://dx.doi.org/10.3311/pptr.13521.
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Rider visibility has been considered an important issue in car-motorcycle accidents due to the frequency of unperceptive and negligent driving behavior. Mostly car drivers stated that they didn’t see the rider before the collision. This study aims to investigate the contributing factors that reduce rider visibility in point of view of car drivers for car-motorcycle accidents. The study considered the car-motorcycle accidents which occur in the built-up area. Statistical data analysis categorized the accidents into six dominant accident types. Twenty-five accidents were selected by accident types for simulation analysis. The selected accidents were simulated in Virtual Crash software during 5 seconds before the collision. The simulation analysis evaluated that the view obstructions, blind spots and high speed were the contributing factors that reduce rider visibility in car-motorcycle accidents. The simulation plots identified the involvement of contributory factors and variation within the time interval. The comprehensive in-depth analysis also evaluated that no collision avoidance maneuvers were performed by most of the car drivers due to visibility issues before the collision. Safety systems were proposed based on observed factors according to car and motorcycle perspectives for collision avoidance.
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Chen, Menghuan, Jiarong Liang, Jingyao Shu, and Ren Zhu. "A Hierarchical Accident Recognition Method for Highway Traffic Systems." Journal of Physics: Conference Series 2456, no. 1 (March 1, 2023): 012034. http://dx.doi.org/10.1088/1742-6596/2456/1/012034.
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Abstract With the rapid development of highway traffic systems, the accident rate of highway remains high. Once an accident occurs, it will cause serious casualties. In this paper, we proposed a highway accident identification method for real-time accident detection through high-speed monitoring. The proposed method focuses on high detecting speed and high recall for car accident. The designed system consists of four parts: first, yolov3 is used to detect the car. Second, identified car frames are put the identified car frame into the Mosse Tracker for tracking. Third, speed and future center point are estimated according to its size and pixel movement. If the speed exceeds a certain threshold or the future center points among other trackers are too close, the object frame will be put into a trained SVM model. Finally, the flow vector estimation calculation will be used to determine whether there is an accident. The system has fast detection speed, high recall and strong real-time performance. Through this system, we can send warning information to upstream car drivers and traffic management personnel to improve the efficiency of accident handling and reduce the rate of highway traffic accidents and casualties.
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Rajani Devi, M., and V. Jyothi. "Accident & Alcohol Detection Surveillance Robot." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012065. http://dx.doi.org/10.1088/1742-6596/2089/1/012065.
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Abstract Nowadays in India many accidents are occurred due drunk and drive. So, we have made a project to resolve and predict these incidents, in which we used Arduino, Alcohol sensor, GSM and GPS modules. Here the Alcohol sensor is placed inside the car which will detect whether the driver of the car has drunk or not. If the driver is drunk then the Alcohol sensor will get triggered and the car engine will not turn ON or it will OFF the engine if it is already in ON state. Here the other two modules which are GSM and GPS. If the car has met with an accident the limit switch which is present inside the car will trigger the Arduino by which an SMS will be sent to the particular contacts which are been saved in the Arduino program. The GPS will be connected to the number of satellites so that when the car met with the accident the live location of the car will be sent to the contact through the GSM module. This entire setup of GPS and GSM will be kept inside the car. This entire project can reduce the accidents which are occurred by the drunk and drive and dead corners and also predict the location of the accidents. It results in increasing the human safety and protection
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Sokolovskij, Edgar, and Edvinas Juodka. "Research on the Circumstances of a Car–Cyclist Collision, Based on the Trajectory of the Cyclist’s Movement after the Collision." Sensors 22, no. 17 (August 23, 2022): 6324. http://dx.doi.org/10.3390/s22176324.
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This article examines a simulated collision between a car and a cyclist, assessing the trajectory of the cyclist’s movement after the impact, namely the throwing distances and angles of the cyclist and bicycle. Information about the car and cyclist models used for the study is provided. Special software PC CRASH 8.1 for the analysis and reconstruction of traffic accidents was used to simulate a car–cyclist collision. Simulations of car–cyclist collisions were carried out, with different speeds for the car and the cyclist, and locations at the time of the impact. The movement of a bicycle after a crash tends to be irregular and is dependent on various parameters that are usually not possible to evaluate. Therefore, the parameters of the movement of the bicycle after the collision (the throwing angle and the distance) usually do not allow determination of the speed of the car before the accident. The movement of the cyclist after impact was more informative for determining the speed of the car before the accident. For example, when there was an angle of 30°, 60°, or 90° between the longitudinal axes of the car and the cyclist, there was a clear dependence between the speed of the car and the cyclist’s throwing distance, and usually also between the speed of the car and the cyclist’s throwing angle. Thus, in such cases, it is possible to determine approximately the initial speed of the car before the collision, based on the trajectory of the cyclist’s movement after the impact, namely his throwing distance and angle. In cases of real traffic accidents, with knowledge of the location of the car–cyclist collision and the position of the cyclist after the traffic accident, the speed of the car before the accident can be determined according to the abovementioned dependencies. Thus, the proposed methodology could be used in the reconstruction and examination of traffic accidents.
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Gebre Meles, Hagazi, Desta Brhanu Gebrehiwot, Fireweini Gebrearegay, Gebretsadik Gebru Wubet, and Teodros Gebregergis. "Identification of Determinant Factors for Car Accident Levels Occurred in Mekelle City, Tigray, Ethiopia: Ordered Logistic Regression Model Approach." Momona Ethiopian Journal of Science 13, no. 2 (April 11, 2022): 225–39. http://dx.doi.org/10.4314/mejs.v13i2.3.
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The car accident injury level is known to be a result of a complex interaction of factors to drivers’ behavior, vehicle characteristics, and environmental condition. Therefore, it is obvious that identifying the contribution of the factors to the accident injury is very critical. The objective of the study was to perform a descriptive analysis to see the characteristics of car accidents, and to assess the prevalence and determinants of road safety practices in Mekelle City, Tigray, Ethiopia. A random sample of data was extracted from the traffic police office from September 2014 to July 2017. An ordered logistic regression model was used to examine factors that worsen the car accident level. A total sample of 385 car accidents was considered in the study of which 56.7% were fatal, 28.6% serious, and 14.7% slight injury. The model estimation result showed that being experienced drivers (Coef. = 0.686; p-value< = 0.050) were found to increase the level of injury. On the other hand, being private vehicle (Coef. = -1.160; p-value <= 0.010), the type of accident of vehicle with pedestrian (Coef. = -2.852; p-value <= 0.010), being heavy truck (Coef. = -0.656; p-value <= 0.050), being a cross country bus (Coef. = -0.889; p-value <= 0.050) and being owner of vehicle is the driver himself (Coef. = -.690, p-value <= 0.050) were found to decrease the level of car accident injury severity. In conclusion, it is better to create continued awareness for those who are experienced drivers, who carelessly follow the traffic rules. Special attention is required for government-owned vehicle drivers, as they were found to increase the level of car accident injury through different short-term training.
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R, Padma, and Shilpa H H. "Car Accident Detection and Alert System Leveraging Deep Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 05 (May 30, 2024): 598–610. http://dx.doi.org/10.26562/ijirae.2024.v1105.24.
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This paper presents a novel approach to address the critical issue of car accidents through an advanced Accident Detection and Alerting System (ADAS) empowered by cutting-edge technologies in deep learning, computer vision, and communication APIs. With road safety being a paramount concern worldwide, timely detection of car accidents followed by swift alerting mechanisms can significantly reduce the severity of accidents and save lives. The proposed system architecture incorporates the Ultralytics framework, Convolutional Neural Networks (CNN), You Only Look Once version 8 (YOLOv8), OpenCV, and Twilio API. Initially, YOLOv8 is employed to perform real-time detection of vehicles and potential accident scenarios from live video feeds obtained from roadside cameras or vehicle-mounted cameras. Subsequently, CNN models are utilized for comprehensive analysis of detected objects to discern potential accidents based on predetermined criteria, including sudden deceleration, collision patterns, and anomalous vehicle movements. Upon successful detection of an accident, the system triggers an alert mechanism via the Twilio API, which delivers instantaneous notifications to emergency services, nearby medical facilities, and designated contacts of the involved parties. These alerts are enriched with crucial details such as the accident location, vehicle types involved, and severity assessments derived from CNN analyses. Furthermore, the integration of OpenCV facilitates preprocessing tasks such as noise reduction, image enhancement, and object tracking, thereby enhancing the system's robustness and accuracy in diverse environmental conditions and lighting scenarios. The proposed ADAS offers numerous benefits, including real-time accident detection, automated alerting of emergency services, and minimized response times, ultimately contributing to mitigating the severity of accidents and preserving lives. By harnessing the power of deep learning algorithms, advanced computer vision techniques, and seamless communication APIs, this system represents a significant step towards fostering safer road environments within smart cities and transportation networks.
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Li, Fan, Honggeng Li, Fuhao Mo, Sen Xiao, and Zhi Xiao. "INVESTIGATION ON RISK PREDICTION OF PEDESTRIAN HEAD INJURY BY REAL-WORLD ACCIDENTS." Transport 34, no. 3 (June 11, 2019): 394–403. http://dx.doi.org/10.3846/transport.2019.10410.
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Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians’ head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design.
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Ksenofontova, Victoria A., and Larisa A. Kiyashko. "MODELING BEHAVIOR OF CAR BODY STRUCTURAL ELEMENTS UNDER THE IMPACT OF WORKING AND EXTREME LOADS." International Journal of Advanced Studies 12, no. 3 (September 30, 2022): 40–50. http://dx.doi.org/10.12731/2227-930x-2022-12-3-40-50.
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The article is devoted to the study behavior of car structural elements during operation and in extreme conditions that occur during road traffic accidents (RTA).
At present, a promising direction of research in this area is the development methods based on the analysis of structural elements of a damaged car deformed as a result an accident and the restoration of the energy exchange pattern upon impact.
Research carried out both theoretically and experimentally. Theoretical studies based on the methods of theoretical and applied mechanics, strength of materials and elasticity theory.
The goal is to improve the methodology for conducting an autotechnical examination of an accident.
Method or methodology of work: solving inverse problems of continuum mechanics.
Results: a technique developed for determining the initial speed of a car before an accident based on the results measuring deformed structural elements. The accuracy determining the initial speed of the car is proportional to the accuracy measuring the deformations damaged car.
Scope results: it is advisable to apply the obtained results when conducting an autotechnical examination of an accident.
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Pradyavita, Vicitra, Muhammad Zainul Arifin, and Sobri Abusini. "Model Peluang Kecelakaan Mobil Penumpang pada Ruas Jalan Nasional Gempol Kabupaten Pasuruan." Rekayasa Sipil 17, no. 2 (May 3, 2023): 230–36. http://dx.doi.org/10.21776/ub.rekayasasipil.2023.017.02.16.
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The increase in accidents is caused by a vehicle, humans, roads, and environmental factors. On the gempol national road in Pasuruan,Regency,0this;study;aims,to;identify;the;characteristics;of,car,drivers, roads, and opportunity models that cause accidents. Data analysis using Structural Equation Modeling (SEM) included 243 questionnaires of drivers who had experienced accidents. Road Characteristics based on user perceptions that focus on geometric conditions at the accident site are the most prevalent accident modeling results that affect accident intensity. If we want to reduce accident rates, we should prioritize addressing geometric conditions at accident sites, as well as socializing traffic awareness and installing signs in accident-prone areas.
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Pompili, Maurizio, Paolo Girardi, Giulia Tatarelli, and Roberto Tatarelli. "Suicidal Intent in Single-Car Accident Drivers." Crisis 27, no. 2 (March 2006): 92–99. http://dx.doi.org/10.1027/0227-5910.27.2.92.
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This study addressed the issue of a possible link between single-car accident drivers and suicidal intent. In the international literature this topic has generated both positive and negative results. Some authors have stressed unconscious suicidal motivations in various single accidents. Nevertheless, refutation of such theory was demonstrated through experimental studies. We selected 30 single-car accident drivers who had been admitted to emergency departments and then hospitalized for an average period of 10 days. We administered the Reason for Living Inventory (RFL) and investigated their attitudes toward suicide. We also matched these patients with a control group of drivers who had never had a car accident. Results showed that single-car accident drivers were not exposed to a higher overall suicidal risk, though they reported a higher risk on the RFL Survival and Coping Beliefs subscale and often expressed tiredness of being alive. These patients had experienced more life events than controls. We conclude that, although suicide risk was low in our patients, they were engaged in looking for a solution to their problems in which the accident played a role in such a process. This behavior has some characteristics of the logic of suicidal individuals.
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McKenzie, Andrew, Daniel Harbour, and Laurel J. Watkins. "A Curious Car Accident." International Journal of American Linguistics 88, S1 (April 1, 2022): S223—S233. http://dx.doi.org/10.1086/718246.
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Nguyen, Thomas, Marie-Luce Chirade, Apostolos C. Agrafiotis, George El Khoury, Benjamin Tatete, Marjorie Beumier, and Jean-Louis Vincent. "A lucky car accident." CJEM 15, no. 04 (July 2013): 237–38. http://dx.doi.org/10.2310/8000.2012.120781.
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Djuric, Predrag, and Marica Miladinov-Mikov. "Some characteristics of drivers having caused traffic accidents." Medical review 61, no. 9-10 (2008): 464–69. http://dx.doi.org/10.2298/mpns0810464d.
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Introduction. Traffic accidents are caused by road, vehicle and human factors, the latter one causing, either by itself or associated with other factors, more than 90% of car accidents. There are three types of human errors: errors in perception, attention and memory. Material and Methods. The study included thirty healthy drivers, aged 28-40, with 500 km driven per a week, who had caused at least one traffic accident, but not being intoxicated by alcohol or drugs during the accident. The same number of controls were included. Both the cases and controls were interviewed. Results. We found no difference regarding the marital status, education, conflicts with family members or problems at work, frequency of using the car, speed and habit of fastening seat belts among car drivers who had caused traffic accidents and those who had not. However, we did find significant differences in the frequency of alcohol consumption in general and before driving. Discussion. Car drivers who had caused traffic accidents used alcohol regularly in greater percentage than those drivers who had not caused any accident - i.e. not a single control claimed to use alcohol regularly. A statistically significant difference was also recorded in alcohol consumption immediately before driving. Such results had been expected, bearing in mind the influence of alcohol on the time needed by drivers to react in emergency. Conclusion. Alcohol consumption is an important factor causing traffic accidents. Since drivers included in our study did not cause a road accident after consuming alcohol, we have found that alcohol consumption, both regular and just before driving, is an important cause of traffic accidents.
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Echab, H., N. Lakouari, H. Ez-Zahraouy, and A. Benyoussef. "Cellular automata model simulating traffic car accidents in the on-ramp system." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550100. http://dx.doi.org/10.1142/s0129183115501004.
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In this paper, using Nagel–Schreckenberg model we study the on-ramp system under the expanded open boundary condition. The phase diagram of the two-lane on-ramp system is computed. It is found that the expanded left boundary insertion strategy enhances the flow in the on-ramp lane. Furthermore, we have studied the probability of the occurrence of car accidents. We distinguish two types of car accidents: the accident at the on-ramp site (Prc) and the rear-end accident in the main road (Pac). It is shown that car accidents at the on-ramp site are more likely to occur when traffic is free on road A. However, the rear-end accidents begin to occur above a critical injecting rate αc1. The influence of the on-ramp length (LB) and position (xC0) on the car accidents probabilities is studied. We found that large LB or xC0 causes an important decrease of the probability Prc. However, only large xC0 provokes an increase of the probability Pac. The effect of the stochastic randomization is also computed.
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Golze, J., U. Feuerhake, C. Koetsier, and M. Sester. "IMPACT ANALYSIS OF ACCIDENTS ON THE TRAFFIC FLOW BASED ON MASSIVE FLOATING CAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 95–102. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-95-2021.
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Abstract. The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow.
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Volchatova, I. V. "Road Accidents: Transport and Social Risks." World of Transport and Transportation 14, no. 3 (June 28, 2016): 232–42. http://dx.doi.org/10.30932/1992-3252-2016-14-3-21.
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On the basis of statistics of traffic accidents accident rate on the roads of Irkutsk region was analyzed. It is shown that the majority of accidents with victims occur due to violations of traffic rules by drivers. The dependence of the number of accidents and injuries on time of day was identified. The maximum contribution to accident rate is made by car drivers. represented by all kinds of transport – rail, road, air, inland waterways and a network of railways and roads. The basis of the road network is federal, regional and local roads with the length, respectively, 1,7, 12.6 and 17 thousand. km. More than 912 thous. Cars are registered in the region and many transit cars are moving on them. Thedependence of the number of accidents and injuries on time of day was identified. The maximum contribution to accident rate is made by car drivers.
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Factor, Roni, Gad Yair, and David Mahalel. "Who by Accident? The Social Morphology of Car Accidents." Risk Analysis 30, no. 9 (September 2010): 1411–23. http://dx.doi.org/10.1111/j.1539-6924.2010.01423.x.
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Abdulmajeed Alamri, Tarek M. Esmael, Sami Fawzy, Hany Hosny, Saleh Attawi, Ahmed Asiri, and Arulanantham Zechariah Jebakumar. "A study on Private Car Accidents." International Journal of Novel Trends in Pharmaceutical Sciences 11, no. 1 (March 27, 2021): 10–24. http://dx.doi.org/10.26452/ijntps.v11i1.1409.
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In this study, road traffic injury (RTI) was defined as any injury resulting from a road traffic accident irrespective of severity and outcome. Road traffic accident (RTA) was defined as any crash on the road involving at least one moving vehicle, irrespective of it resulting in an injury. This could include collision with a vehicle or any non`moving object while driving/riding a vehicle, collision with a moving vehicle while walking/running/standing/ sitting on the road, or fall from a moving vehicle. The burden of road traffic accidents (RTA) is a leading cause of all trauma admissions in hospitals worldwide. Road traffic injuries cause considerable economic losses to victims, their families, and to nations as a whole. These losses arise from the cost of treatment (including rehabilitation and incident investigation) as well as reduced/lost productivity (e.g. in wages) for those killed or disabled by their injuries and for family members who need to take time off work (or school) to care for the injured. Road traffic fatality in the Kingdom of Saudi Arabia (KSA) is the highest, accounts for 4.7% of all mortalities. Road injuries also are reported to be the most serious in this country, with an accident to injury ratio of 8:6. In this study, we try to focus on some causes of the accidents in KSA, so we can implement the prevention plan.
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Wahab, Wilton, Angelalia Roza, Markis Febi Ramadhani, and Yessy Yusnita. "Analisis Kecelakaan Lalu Lintas Ruas Jalan Padang - Solok dengan Metode Accident Rate." Jurnal Ilmiah Rekayasa Sipil 20, no. 2 (November 3, 2023): 234–43. http://dx.doi.org/10.30630/jirs.v20i2.1216.
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The Padang-Solok road section is one of the national road sections that frequently experiences accidents every year. The aim of this research is to determine the factors that cause accidents on the Jalan Lubuk Paraku Sitinjau Lauik section based on the accident rate, and find out alternative prevention solutions future traffic accidents. This type of research is descriptive research using the Accident Rate method. The factors reviewed are the total number of accidents that occur and the length of the road section studied. An Accident Rate value > 1.0 will be classified as a Black Spot, while a value < 1.0 will be classified as a Black Site. The results of the accident analysis based on the Accident Rate can be identified in three road sections that show a high accident rate, namely more than 1.0. The highest accident rate was on the Panorama 2 – Padang-Solok City Limits road section at 2.37 accidents/km.year. The most dominant factor causing traffic accidents is the human factor. After an inspection was carried out, the brakes failed due to the large number of descents or steep inclines which made the car brakes not function properly. An alternative solution for preventing accidents is to create a temporary stopping place at the Padang - Solok city limits to cool down the engine and car brakes, repair traffic signs that are not yet permanent and create a special brake rescue lane for broken brakes at Panorama 2 and near the rice fields PU Mes.
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Nagurnas, Saulius, Valentinas Mickūnaitis, Robertas Pečeliūnas, and Aurelijus Vestartas. "ANALYSIS OF CALCULATION METHODS USED FOR ACCURACY EVALUATION OF THE RESULTS OF ROAD ACCIDENT EXAMINATION." TRANSPORT 23, no. 2 (June 30, 2008): 156–60. http://dx.doi.org/10.3846/1648-4142.2008.23.156-160.
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For examination of road accidents often calculations are being performed according to mathematical equations having many variables, real values of which are of stochastic nature and they depend on many accident factors. Therefore it is important to know limits of determination of final values of every being analysed parameter of car movement and their probabilistic characteristics. This paper presents some cases of simulation of some road situations by mathematical methods of calculations, which now are widely used for evaluation of accuracy of values of the calculated parameter (in this paper ‐ the braking distance). Also here is presented some analysis of these methods. The paper presents the following cases of car braking: braking with making attempts to prevent a collision with another car, braking through impact‐to‐pedestrian prevention and conventional (rectilinear) car braking. The main attention in the analysis of the mentioned road situations is paid to veracity of the car braking distance, i.e. to limits of variation of values of this parameter and also to influence of accidental factors on the final result which is being looked for. Some conclusions on the expediency of application of the described methods of calculations to analysis of road accidents are present.
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Reinaldy, Gerardo, Peter Rhatodirdjo Angka, Albert Gunadhi, Hartono Pranjoto, Yuliati, and Rasional Sitepu. "Environmental management for car accident precaution and remote notification." E3S Web of Conferences 475 (2024): 02013. http://dx.doi.org/10.1051/e3sconf/202447502013.
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Cars have been utilized as reliable means of transportation beyond the capacity of motorcycles. However, the misuse and abuse of these vehicles through unsafe practices like speeding and reckless driving poses a great risk that results in accidents – and sometimes – with casualties. In such unfortunate circumstances, the importance of prompt accident notification is extremely important for the driver. This notification should encompass vital accident details and precise location information. Thus, the implementation of a comprehensive system for accident warnings and the remote transmission of accident data emerges as an imperative safety measure. In this system, accidents are assessed based on the vehicle's slope relative to the road and speed. The vehicle's slope is measured using ultrasonic sensor, which calculates the ground clearance distance at two different locations of the vehicles. The two sensors will provide warning when there is a difference of more than 3 cm. Speed is measured using GPS, with the limit set at 90 km/h. If crashed occurs, Airbag Sensor gives logic ‘1’, then GPS will take current coordinates and date of the accident, then sends it via SIM800L in SMS format to registered number. Thus, the dangerous condition signed by 4,7 cm difference marked by Red LED and formed 7o angles which caused by excessive maneuver while GPS has 95% accuracy with speedometer. Thus, this voice output sure be great while quiet environment which output quality is 45dB at conversation environment.
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Yamada, Yasunori, Kaoru Shinkawa, Masatomo Kobayashi, Hironobu Takagi, Miyuki Nemoto, Kiyotaka Nemoto, and Tetsuaki Arai. "Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study." Journal of Medical Internet Research 23, no. 4 (April 8, 2021): e27667. http://dx.doi.org/10.2196/27667.
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Background With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. Conclusions Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.
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Benea, Bogdan, and Adrian Soica. "The Contact Phase in Vehicle–Pedestrian Accident Reconstruction." Applied Sciences 13, no. 16 (August 18, 2023): 9404. http://dx.doi.org/10.3390/app13169404.
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The need for continuous research to refine the models used in forensic accident reconstruction appears with the development of new car models that satisfy consumer complaints. This paper analyzed a sub-sequence of car and pedestrian accidents from the perspective of the distance traveled by them in the contact phase with the aim of improving the information regarding the reconstruction of road accidents. This research included the analysis of some real tests with pedestrian dummies, as well as simulations of the impact between different classes of vehicles and pedestrians in two different walking positions. Specialized software was used with complex multibody models of pedestrians, modifying the speed and deceleration parameters of the car at the time of the collision. For pedestrian characteristics, the friction coefficients of the ground, car and its mass were modified. The research results highlight the differences between the bilinear models used in accident reconstruction and the proposed study. They can also be used to determine the distance traveled by the vehicle in the first phase of a collision with pedestrians.
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Yu, Ye, and Zhiyuan Liu. "A data-driven on-site injury severity assessment model for car-to-electric-bicycle collisions based on positional relationship and random forest." Electronic Research Archive 31, no. 6 (2023): 3417–34. http://dx.doi.org/10.3934/era.2023173.
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<abstract> <p>Vulnerable road users (VRUs) are usually more susceptible to fatal injuries. Accurate and rapid assessment of VRU injury severity at the accident scene can provide timely support for decision-making in emergency response. However, evaluating VRU injury severity at the accident scene usually requires medical knowledge and medical devices. Few studies have explored the possibility of using on-site positional relationship to assess injury severity, which could provide a new perspective for on-site transportation professionals to assess accident severity. This study proposes a data-driven on-site injury severity assessment model for car-to-electric-bicycle accidents based on the relationship between the final resting positions of the car, electric bicycle and cyclist at the accident scene. Random forest is employed to learn the accident features from the at-scene positional relationship among accident participants, by which injury severity of the cyclist is assessed. Conditional permutation importance, which can account for correlation among predictor variables, is adopted to reflect the importance of predictor variables more accurately. The proposed model is demonstrated using simulated car-to-electric-bicycle collision data. The results show that the proposed model has good performance in terms of overall accuracy and is balanced in recognizing both fatal and non-fatal accidents. Model performance under partial information confirms that the position information of the electric bicycle is more important than the position information of the cyclist in assessing injury severity.</p> </abstract>
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Dumbrava, Dan Perju, Carmen Corina Radu, Ovidiu Chiroban, Diana Bulgaru-Iliescu, Tatiana Iov, Simona Irina Damian, and Daniel Ureche. "Retrograde Extrapolation of Blood Alcohol Concentration A theoretical and practical approach." Revista de Chimie 70, no. 2 (March 15, 2019): 407–9. http://dx.doi.org/10.37358/rc.19.2.6925.
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The influence of high blood alcohol levels in vehicle drivers involved in car accidents is a growing process, despite the legal punishments which have been already increasing in severity. Therefore, determining the level of blood alcohol concentration at the moment of the car accident is essential in establishing the legal responsibility of the driver. However this exact determination is in most of the cases difficult, due to the fact that between the moment of the car accident and the collecting of the blood in a medical unity a varying time frame passes, depending essentially of the distance from the place of the car accident and the hospital. Even more, from a juridical point of view, at the moment the blood is taken from the driver in the hospital, this is not representing a social danger anymore because he is not driving. Hence, the retrograde extrapolation of blood alcohol concentration is and should be the legal tool of providing the juridical system a valid answer at the question implying the value of the blood alcohol concentration at the moment the antisocial deed (car accident) had been made. The aim of this article is to theoretically present the scientific method of retrograde extrapolation of blood alcohol concentration and also to highlight its strong and weak points.
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Lupita, Anggun, M. Yasin Ardhy, and Muhammad Fahruddin. "PERLINDUNGAN HUKUM TERHADAP KONSUMEN PENGGUNA JASA TRANSPORTASI ONLINE." Jurnal Hukum Jurisdictie 1, no. 2 (August 5, 2019): 13–33. http://dx.doi.org/10.34005/jhj.v1i2.13.
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Anggun Lupita, 2018, Legal Protection against Users of Online Transportation Services, Thesis, Faculty of Law, Islamic University as-Shafi'iyah, Jakarta.
This research aims to find out the implementation of legal protections for consumers of online transportation service users who experience losses or accidents and dispute resolution of users of online transportation services. The purpose of this study is to find out how to implement legal protections for consumers of online transportation service users who experience losses or accidents. Formulation of the problem: (1) How to implement legal protections against consumers of online transportation service users. (2) How to resolve disputes with users of online transportation services. Company PT. Go-Jek as an online transportation provider company must provide compensation or insurance to consumers of online transportation service users in the form of Go-Car who have an accident, stipulated in Article 237 of Law No. 22 of 2009 on Traffic and Road Transportation expressly requires public transportation companies to follow the accident insurance program as a form of responsibility for insurance guarantees for accident victims. In the implementation of legal protections for consumers of online transportation service users who have a Go-Car accident, PT. Go-Jek as a transportation provider of Go-Jek only provides compensation or insurance of Rp 10,000,000.00 and hospital costs of Rp 5,000,000.00, as a form of responsibility and compensation to consumers who have experienced accidents. In the settlement of disputes of users of online transportation services that occur in go-car accident cases in Manado that occur by consumers are out of court (non-litigation) or familially in accordance with the results of the agreement of the parties. The conclusion of this study according to the authors of the implementation of compensation in the form of compensation or insurance to consumers of go-car online transportation service users as victims of accidents is unfair and not in accordance with the Regulation of the Minister of Finance No. 16 of 2017 on Large Compensation and Mandatory Contribution of Road Traffic Accident Fund, compensation for victims of road traffic accidents.
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Naufal, Muhammad Alwan, and Abba Suganda Girsang. "Traffic accident classification using IndoBERT." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 1 (April 4, 2024): 42. http://dx.doi.org/10.11591/ijict.v13i1.pp42-49.
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<span>Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.</span>
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Evtukov, Stanislav, Yaroslav Vasilev, Vseslav Voronin, and Egor Golov. "Differentiating the stiffness of the side of the car." MATEC Web of Conferences 341 (2021): 00035. http://dx.doi.org/10.1051/matecconf/202134100035.
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Expertise on the materials of road accidents (road accidents), calculate the speed of the vehicles involved in the accident by analyzing the resulting deformation damage by the average stiffness coefficients of the vehicle bodies. But this entails the presence of an error in the calculation and, as a result, not a correct result due to the fact that the internal structure of the side of the car is heterogeneous – the stiffness in each of its areas is different. To eliminate possible errors when using the averaged coefficients, the article offers a method for calculating the stiffness coefficients for various areas of the side of cars that have received deformations as a result of an accident.
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Sheikh, Mohammad M. R. "Statistical Modelling of Road Traffic KSI Car Accidents in England (STATS19)." International Journal of Mathematics and Statistics Studies 10, no. 4 (April 15, 2022): 14–47. http://dx.doi.org/10.37745/ijmss.13/vol10n41447.
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Several generalised linear models for counts (i.e., Poisson Model) as well as for binary response (i.e., Binary Logistic Model) and ordinal response (i.e., Ordinal Logistic Model) depending on selected multiple explanatory factors (discrete/ categorical) were developed for the road KSI car accidents in England based on STATS19 data (that were manipulated and several new factors were created), after exploratory exploration of discrete/ dichotomous/ nominal/ ordinal factors applied graphical EDA techniques followed by univariate ANOVA/ ANCOVA as well as MANOVA/ MANCOVA based on same selected multiple explanatory factors. Only the main effects as well as two-way interactions were investigated. Majority of main effects and several interaction effects in GLM models were found statistically significant with greater or lesser likelihood of having consequences. The statistically significant KSI car accident factors were identified and quantified for leading to aims to reduce as well as to prevent the car accident, particularly the killed or seriously injured car accidents. It also leads to inform the policymakers on how best to reduce the number and severity of car crashes.
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Asman, Satri, Werman Kasoep, and Nefy Puteri Novani. "Rancang Bangun Sistem Pendeteksi Kecelakaan Dan Tracking Lokasi Mobil Berbasis Android." CHIPSET 2, no. 02 (October 31, 2021): 7–14. http://dx.doi.org/10.25077/chipset.2.02.7-14.2021.
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This system is designed to know a car accident has happened and also to track a car location,when the accident happened, the system will send accident’s location data to user/familyAndroid application. Thus, user/family knows the accident and they can call the authorities orambulance as soon as possible to help accident’s victim. This system uses some componentssuch as Arduino Uno as a microcontroller, accelerometer sensor MMA7361 as an accelerationdetector, SIM808 Module as a GSM/GPRS network provider and also as GPS sensor. Thissystem tracks the car’s location continuously then the location will be displayed on maps ofAndroid application in the form of a blue marker. Based on this research the accelerationvalues of x-axis and y-axis of the accelerometer which are included in the accident category,which is ≥9g, when an accident occurs the system will send an accident notification toAndroid application and displayed on maps of Android application in the form of a redmarker, to inticipate of no internet connection an SMS with Google maps’s link of accidentlocation is also sent to the user/family.
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Asman, Satri, Werman Kasoep, and Nefy Puteri Novani. "Rancang Bangun Sistem Pendeteksi Kecelakaan Dan Tracking Lokasi Mobil Berbasis Android." CHIPSET 2, no. 02 (October 31, 2021): 47–54. http://dx.doi.org/10.25077/chipset.2.02.47-54.2021.
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This system is designed to know a car accident has happened and also to track a car location,when the accident happened, the system will send accident’s location data to user/familyAndroid application. Thus, user/family knows the accident and they can call the authorities orambulance as soon as possible to help accident’s victim. This system uses some componentssuch as Arduino Uno as a microcontroller, accelerometer sensor MMA7361 as an accelerationdetector, SIM808 Module as a GSM/GPRS network provider and also as GPS sensor. Thissystem tracks the car’s location continuously then the location will be displayed on maps ofAndroid application in the form of a blue marker. Based on this research the accelerationvalues of x-axis and y-axis of the accelerometer which are included in the accident category,which is ≥9g, when an accident occurs the system will send an accident notification toAndroid application and displayed on maps of Android application in the form of a redmarker, to inticipate of no internet connection an SMS with Google maps’s link of accidentlocation is also sent to the user/family.
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Marianingrum, Dyah, Kasih Purwati, and Muhammad Raflin Ilhami Pratama. "GAMBARAN POLA LUKA PADA KORBAN KECELAKAAN LALU LINTAS DI RUMAH SAKIT UMUM DAERAH RAJA AHMAD TABIB PROVINSI KEPULAUAN RIAU PERIODE JANUARI-DESEMBER TAHUN 2022." Zona Kedokteran: Program Studi Pendidikan Dokter Universitas Batam 14, no. 1 (April 8, 2024): 17–25. http://dx.doi.org/10.37776/zked.v14i1.1362.
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Background: Traffic accidents are a leading cause of death in developing countries, and the incidence continues to rise with population growth and increased transportation mobility. This study aims to analyze the pattern of injuries in traffic accident victims treated at Raja Ahmad Tabib Regional General Hospital, Riau Islands Province, during the period from January to December 2022. Methods: This study utilized a retrospective descriptive design with a cross-sectional approach. The population consisted of 1,533 traffic accident victims during the January-December 2022 period, and a sample of 318 individuals was selected using purposive sampling. Data analysis was conducted using descriptive statistics. Results: The study revealed that 69.5% of the victims were aged over 30 years, 66.7% were male, and 84.0% survived the accidents. The most common injury pattern among pedestrians was abrasions (37.9%), motorbike riders most frequently experienced abrasions (48.0%), car drivers mostly suffered from lacerations (43.4%), and car passengers predominantly had contusions (50.0%). Conclusion: This research concludes that the injury patterns in traffic accident victims at Raja Ahmad Tabib Regional General Hospital in the Riau Islands Province during 2022 show that abrasions are dominant among motorbike riders, lacerations are predominant among car drivers, and contusions are prevalent among car passengers.
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Mahajan, Ayush, Ved Upadhye, Vaidehi Dhobale, Rohit Dhongade, and Anuja Ghasad. "Review on Automated Accident Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 679–82. http://dx.doi.org/10.22214/ijraset.2023.56056.
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Abstract: Major accidents on highways, freeways and local roads can lead to huge social and economic impacts. Minor accidents may be resolved by the passengers themselves and do not require escorting to hospitals whereas major accidents where airbags are deployed require the immediate attention of authorities. Automated Accident Detection System [AADS] is an auto-detection unit system that immediately notifies an Emergency Contact through an alert alarm when someone presses the trigger button present on poles having CCTV cameras in the end vehicle is detected by the system, detailing the location and time of the accident. The idea is that as soon as an accident is detected by the system, the authorities should immediately be notified to prevent further car congestion as well as allow the passengers to be escorted to the hospital within time. A complete system would automatically detect and record traffic conditions associated with accidents such as the time of the accident, video of the accident, and the traffic light signal controller parameters. The basic research required to develop the system is considered. This involves developing methods for processing acoustic signals and recognizing accident events from the background traffic events. A database of vehicle crashes, car braking, construction, and traffic sounds was created. The meal frequency cepstral coefficients were computed as a feature vector for input to the classification system. A neural network was used to classify these features of the crash and non-crash events.
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Esmaeeli, Hamed, Iman Abbaszadehfallah, Othman Bin Chepuan, and Seyed Hossein Hosseini. "Road Accident Analysis Factors." Applied Mechanics and Materials 253-255 (December 2012): 1741–46. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1741.
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A large number of fatalities on road are occurred by vehicle accident. One of most significant causes of accident on road is increasing car ownership in last decade. The study is conducted to determine a relationship between car ownership and accident rate. The most effective parameters on increasing accident rate in developed countries are studied. This paper investigates a model that forecast the rate of accident on based on previous collected car ownership data. Meanwhile, those parameters can change the rate of car ownership such as income, distance between residential area and work zone, the number of employee over family size and registered car over employee are studied. The study is conducted on based on Australia data and Robust- regression techniques are used to analyse the data. The result shows robust regression reached the most effective coefficient to reduce fatalities on road.
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Patel, Priya, and Rupali Shekokar. "Advanced Safety & Awareness System for Car Drivers." Journal of Electronics and Communication Systems 8, no. 2 (July 20, 2023): 11–18. http://dx.doi.org/10.46610/joecs.2023.v08i02.002.
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With the recent development in motorization brought on by expanding population and wider penetration of automobiles across the country, road accidents are rapidly causing loss of lives and serious injuries. The main cause of mortality for those between the ages of 15 and 49 is related to traffic accidents, which are the top cause of death worldwide. Up to this point, there has been a significant amount of research conducted on predicting and locating vehicle accidents; nevertheless, there has not been any advance indication of the accidents to the drivers. The purpose of this study is to get an understanding of the numerous methodologies that have contributed to the reduction of accidents, particularly through the processes of preventing and identifying accidents. A study on various suggested strategies using a number of techniques for the stages that are included has been done in order to determine an appropriate accident detection and alert system that is accurate and effective. This study also examines the advantages and disadvantages of each of these methodologies. System requirements are developed after conducting an in-depth review of both currently available options and the relevant literature. A thorough examination and evaluation of numerous systems is done that have significantly improved accident detection and alerting.
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Li, Tinglang. "Research on the Troubleshooting of “Yanmar 6EY26L” Diesel Generator." Journal of Physics: Conference Series 2029, no. 1 (September 1, 2021): 012155. http://dx.doi.org/10.1088/1742-6596/2029/1/012155.
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Abstract The Flying Accident of marine generator is not common. If the flying accident happens in the power grid, it will easily cause the whole ship power loss and engine damage, which will have a great influence on the safe navigation of ships. this paper analyzes and probes into the causes of the flying car accident occurred in 2019 in a round of “Yanmar 6EY26L” type diesel generator, and expounds the correct treatment measures for the flying car according to the treatment methods of the ships, finally, the daily management method of ship diesel generator, the precautions and preventive measures during diesel generator start-up are put forward in order to avoid similar accidents.
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Hafeez, Farrukh, Usman Ullah Sheikh, Saud Al-Shammari, Muhammad Hamid, Abdul Baqi Khan Khakwani, and Zeeshan Ahmad Arfeen. "Comparative analysis of influencing factors on pedestrian road accidents." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 257–67. http://dx.doi.org/10.11591/eei.v12i1.4312.
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Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.
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Danda, Junio Andika, Ade Silvia Handayani, Sopian Soim, Nyayu Latifah Husni, and Leni Novianti. "Alat Monitoring Kecelakaan Dengan Intelligent Transport System Berbasis Internet of Things." JURIKOM (Jurnal Riset Komputer) 9, no. 4 (August 30, 2022): 1087. http://dx.doi.org/10.30865/jurikom.v9i4.4652.
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Accidents are one of the problems that are always faced in big cities. This is evident from the indications that traffic accident numbers are always increasing. Delays in handling accidents often occur due to delays in information received by the police and nearby hospitals. Therefore, a remote monitoring system is needed to control vehicles that are operating in real time using monitoring tools that are able to detect traffic accidents and emergency events on the way In this study, the Internet of Things-based Intelligent Transport System was applied using the Support Vector Machine method whose design was carried out using accelerometer sensors, vibrating sensors and sound sensors with the addition of NEO 6M GPS as a coordinate point information provider and a PI NoIR Camera to capture images of surrounding conditions when an accident occurred. The way this tool works is that when there is an accident in a vehicle (car) this tool has been installed with various sensors and components that are able to analyze the surrounding situation if this car has an accident, it will send data that will be displayed in the Android application in the form of time data, coordinates, vibration, sound and images.
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Huaman Meza, Angela Denisse, Gian Carlos Meza Soto, Jahir Chuquillanqui Guillen, and Giovene Perez Campomanes. "The odds of accident-type casualties in a Peruvian jungle road." Decision Science Letters 12, no. 2 (2023): 163–68. http://dx.doi.org/10.5267/j.dsl.2023.3.003.
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The current analysis analyzed the odds of casualties by road accidents. Hence, data were classified into tertiles for better research, and accident types were classified into five following the authority methodology: rollovers, crash, roadway departure, special accident, and car capsizing. Multi-logistic regression was employed for the data analysis. This research found that rollover was the most deadly accident, and the crash was the most probable to cause injuries.
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Booker, L., J. Spong, B. Hodge, M. Deacon-Crouch, M. Bish, J. Mills, and T. Skinner. "P072 Sleep, Shift work Patterns and Work-Related Accidents in Regional/Rural versus Metropolitan Healthcare Shift Workers." Sleep Advances 4, Supplement_1 (October 1, 2023): A62. http://dx.doi.org/10.1093/sleepadvances/zpad035.157.
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Abstract Introduction Sleep impairment can decline alertness and performance and increase the risk of occupational injuries such as medical errors, workplace injuries and car accidents. Australia has a shortage of healthcare shift workers, particularly in regional/rural areas. The consequence of this is that rural healthcare shift workers potentially have to work longer hours, be more on-call and/or have to commute longer distances to/from work. These factors could impact the worker's sleep and recovery. The aim was to explore the differences in sleep and shift work patterns in regional/rural and metropolitan healthcare shift workers and the risk of workplace accidents, medical error, and car accidents. Method An anonymous online survey was distributed targeting nurses, midwives and paramedics. Results 403 healthcare shift workers completed the survey. Those in regional/rural locations were significantly older, had more experience, worked significantly more hours per week, more night shifts and on-call shifts in the previous two weeks. However, metropolitan shift workers took significantly longer (in minutes) to travel to work, reported more feelings of sleepiness while driving to/from work, having a car accident or near accident while commuting home, and having a workplace accident in the last 12 months. There was however no differences in sleep quality between the groups but poor sleep quality was the main contributing factor related to workplace incidents, medical errors and car accidents. Conclusion There were differences in shift work patterns between metropolitan and regional/rural healthcare shift workers. More exploration is needed to understand the components that contribute to increased OH&S risks.
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Kita-Wojciechowska, Kinga, and Łukasz Kidziński. "Google Street View image predicts car accident risk." Central European Economic Journal 6, no. 53 (April 18, 2020): 151–63. http://dx.doi.org/10.2478/ceej-2019-0011.
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AbstractRoad traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in healthcare, insurance, civil engineering and other industries. We show how images of houses are predictive of car accidents. We analyse 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View and annotate house features such as age, type and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, the public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.
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Loughnan, Margaret. "Recovery from a Car Accident." Homoeopathic Links 25, no. 01 (March 2012): 45–49. http://dx.doi.org/10.1055/s-0031-1298196.
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Ausania, Francesco, Francesca Cittadini, Fidelia Cascini, Matteo Polacco, Vincenzo L. Pascali, and Antonio Oliva. "Decapitation Due to Car Accident." American Journal of Forensic Medicine and Pathology 32, no. 4 (December 2011): 308–9. http://dx.doi.org/10.1097/paf.0b013e31822c8edc.
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Kim, Kwanghwi, Ohhoon Kwon, Taeyeon Kim, Taegeol Lee, Kihoon Choi, Hyun Woo Cho, Doori Kim, et al. "Factors influencing clinical symptoms and treatment of patients with traffic accident injuries: A retrospective chart review with a questionnaire survey." PLOS ONE 16, no. 6 (June 18, 2021): e0252922. http://dx.doi.org/10.1371/journal.pone.0252922.
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This study analyzed factors influencing clinical symptoms and treatment of patients with traffic accident injuries. It used a retrospective chart review and questionnaire survey obtained from 560 patients (266 men and 294 women). It also conducted follow-up observations of progress after car insurance settlements and investigated the usefulness of and patient satisfaction with integrative Korean medicine treatment for traffic accident injuries. Retrospective data of patients admitted for traffic accident injury were obtained. A questionnaire survey was conducted to collect data regarding the degree of traffic accident damage, severity of pain at settlement, any treatment after settlement and duration and cost of such treatment, and patient satisfaction with car insurance services and Korean medicine treatment for traffic accident injury. The results showed no significant association between pain and the degree of damage to the car at the time of traffic accident (P = 0.662), although the degree of damage to the car was more significantly associated with time to reach a car insurance settlement than severity of pain in the patient (P = 0.003). There was no significant association between the degree of damage to the car in a traffic accident and pain after a traffic accident. Greater severity of pain at the time of the car insurance settlement was associated with greater cost and longer time spent in treatment after the car insurance settlement.
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Feizizadeh, Bakhtiar, Davoud Omarzadeh, Ayyoob Sharifi, Abolfazl Rahmani, Tobia Lakes, and Thomas Blaschke. "A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic." Sustainability 14, no. 12 (June 18, 2022): 7468. http://dx.doi.org/10.3390/su14127468.
Full textAbstract:
The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify risk of traffic accident hotspots. Accident data for the time period of April 2018 to November 2020 were obtained from the traffic police of Tabriz (Iran) and analyzed using GIS spatial and network analysis procedures. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number or urban traffic accidents. Eventually, the sustainability of urban transport was analyzed based on the demographic and land use data to identify the areas with a high number of accidents and its respective impacts for the local residences. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road traffic accidents. From the perspective of urban planning, the spatiotemporal urban traffic accident analysis indicated that areas with high numbers of elderly people and children were most affected by car accidents. As we identified the hotspots of urban traffic accidents and evaluated their spatiotemporal correlation with land use and demography characteristics, we conclude that the results of this study can be used by urban managers and support decision making to improve the situation, so that fewer accidents will happen in the future.