Types Of Traffic Accidents Research Articles

Analysis of Traffic Safety Factors and Their Impact Using Machine Learning Algorithms

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

An Influencing Factors Analysis of Road Traffic Accidents Based on the Analytic Hierarchy Process and the Minimum Discrimination Information Principle

Safe traffic is an important part of sustainable transportation. Road traffic accidents lead to a large number of casualties and property losses every year. Current research mainly studies some types of traffic accidents and ignores other types of traffic accidents; therefore, taking various types of road traffic accidents as a whole, an overall study of their influencing factors is urgently needed. To improve road traffic safety, taking various types of road traffic accidents as a whole, this paper analyzes the influencing factors and finds out the causative factors of road traffic accidents. A new index system of road traffic accident influencing factors is constructed based on the existing literature and real traffic data, and their subjective weights and objective weights are obtained by the analytic hierarchy process based on the subjective data and the normalization of the actual traffic data for Yizheng City, Yangzhou, China from January 2020 to December 2020, where the subjective weights are the main weights, and comprehensive weights are obtained by the minimum discrimination information principle correcting the subjective weights with the objective weights. Finally, the global weights, their ranks, and their weight differences are obtained. The main findings are as follows: (1) compared with the real traffic data, experts generally overestimate the impact of road factors on traffic accidents and underestimate the impact of human factors on traffic accidents; (2) in the first-level, human factors and road factors are the causative factors; (3) in the second-level, “motor vehicle drivers’ misconduct”, “road condition”, and “road section” are the causative factors; and (4) in the third-level, “slippery road”, “rain and snow weather”, “intersection”, and “untimely braking” are the causative factors. The research results can provide some scientific basis for improving road traffic safety.

Analyzing traffic conflict scenarios and safety assessment in the context of intelligent vehicles: A research review

The several internal disputes and various types of traffic accidents at an urban crossroads make for a complex traffic scenario. Because accident data and crash-based methodologies are limited, researchers are now examining other safety measures, including traffic conflicts that do not depend on crashes. To better assess road safety in the decades to come new real-time data is essential. The automobile industry is heading toward intelligent cars with the goal of enhancing road safety. Although there is a common failure mechanism between accidents and traffic conflicts during driving, which makes it possible to apply crash frequency and severity estimates to traffic conflict frequency and severity models, predicting traffic conflicts presents distinct difficulties. This study discusses conceptual and methodological concerns while reviewing research papers on traffic disputes as safety measures. It offers a thorough synopsis of previous research from the viewpoint of intelligent vehicles. It explains that there are three primary categories into which the perception technology of intelligent cars may be separated: perception technology, communication technology, and the combination of perception and communication technologies. The paper emphasizes the need of harmonizing international safety standards as well as the role that governments and regulatory agencies play in guaranteeing the security of intelligent vehicles. It highlights the need for additional research on transport conflict modeling techniques and offers practical recommendations for future study.

Cross-Modality Interaction-Based Traffic Accident Classification

Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.

Analizing Traffic Accidents on Pekanbaru-Dumai Toll Road from Road infrastructure Perspective

The Pekanbaru-Dumai (PERMAI) toll road serves a crucial role as the busiest access route in Riau Province. Due to the high volume of traffic on this toll road, it is suspected that various types of traffic accidents occur. This study analyzed the frequency of traffic accidents, accident rate metrics for black-spot and black-site areas, and the condition of road infrastructure on the Pekanbaru-Dumai toll road. This study employed a qualitative descriptive method and collected data from primary sources through field observations and secondary sources, such as accident reports from the Riau Regional Police Traffic Directorate (Indonesian: Direktorat Lalu Lintas POLDA Riau) covering the period from 2020 to 2022 and average daily traffic volume data from the Toll Road Regulatory Agency (Indonesian: Badan Pengatur Jalan Tol [BPJT]). The accident rate formula for black spots and black sites was applied to identify accident-prone and high-traffic areas on the Pekanbaru-Dumai toll road. The analysis results revealed a total of 30 incidents over the three years. The highest accident rate for black-spot areas was 0.10 at KM 34 and KM 35, while the highest for black-site areas was 0.10 at KM 35. Most incidents occurred during the night until early morning, specifically from 18:01 to 02:00. Passenger cars were the most frequently involved vehicles, with 16 single-vehicle collisions. These accidents resulted in 28 victims, including 7 fatalities, 9 serious injuries, and 12 minor injuries. In terms of travel direction, Line A (Pekanbaru to Dumai) experienced 11 incidents, while Line B (Dumai to Pekanbaru) had 19 incidents. The primary factor contributing to these accidents was human error, typically due to negligence or carelessness. Based on observations of road equipment and traffic management on the Pekanbaru-Dumai toll road, the study suggests improving safety by adding noise barriers following Indonesia’s Minister of Transportation Regulation No. 82/2018, Article 33. Additionally, enhancing overall street lighting is recommended, in alignment with Indonesia’s Minister of Public Works Regulation No. 16/PRT/M/2014 concerning Minimum Service Standards.

Predicting Relationship Between Traffic Accidents with Related Injuries

Abstract: Traffic collisions are one of the world's 14 most crucial issues right now since they cause countless fatalities, severe injuries, and financial losses every year. The difficulty of creating accurate forecasting models, Traffic accident severity is important for transportation networks. This study creates models to choose a number of important features and build a model for classifying injury severity. These models are produced utilizing various Machine Learning techniques. The data on traffic accidents, trained and methods for unsupervised machine learning are also used. The primary aim is to draw a connection between the many kinds of injuries and traffic accident types. The study's conclusions imply that unsupervised learning methods may be effective in predicting the severity of harm from traffic accidents.

Trapezoidal Interval Type-2 Fuzzy PIPRECIA-MARCOS Model for Management Efficiency of Traffic Flow on Observed Road Sections

Road infrastructure management is an extremely important task of traffic engineering. For the purpose of efficient management, it is necessary to determine the efficiency of the traffic flow through PAE 85%, AADT and other exploitation parameters on the one hand, and the number of different types of traffic accidents on the other. In this paper, a novel TrIT2F (trapezoidal interval type-2 fuzzy) PIPRECIA (pivot pairwise relative criteria importance assessment)-TrIT2F MARCOS (measurement of alternatives and ranking according to compromise solution) was developed in order to, in a defined set of 14 road segments, identify the most efficient one for data related to light goods vehicles. Through this the aims and contributions of the study can be manifested. The evaluation was carried out on the basis of seven criteria with weights obtained using the TrIT2F PIPRECIA, while the final results were presented through the TrIT2F MARCOS method. To average part of the input data, the Dombi and Bonferroni operators have been applied. The final results of the applied TrIT2F PIPRECIA-TrIT2F MARCOS model show the following ranking of road segments, according to which Vrhovi–Šešlije M-I-103 with a gradient of −1.00 represents the best solution: A5 > A8 > A2 > A1 > A4 > A3 > A6 > A12 > A13 = A14 > A11 > A7 > A9 > A10. In addition, the validation of the obtained results was conducted by changing the values of the four most important criteria and changing the size of the decision matrix. Tests have shown great stability of the developed TrIT2F PIPRECIA-TrIT2F MARCOS model.

Accidentes de tránsito en el Perú, 2012-2018: un problema de importancia para estudiar

Objective: To estimate the prevalence of traffic accidents between 2012 and 2018 in Peru. Material and methods: A retrospective descriptive study was conducted using data on traffic accidents in Peru from the National Institute of Statistics and Informatics (INEI) from 2012 to 2018. The data on traffic accidents in Peru from the National Institute of Statistics and Informatics (INEI) from 2012 to 2018 were reviewed, taking into account the number of injured and deceased for that reason, in order to then calculate the lethality. Similarly, data were obtained on the types of traffic accidents that occurred in the departments of the country in question during 2018. Results: Between the years 2012-2018, 560,745 traffic accidents were reported. During 2012, 94 923 traffic accidents were reported, resulting in 54 572 injured persons, 3 313 fatalities with a fatality rate of 3.49 %. 102 762 traffic accidents were reported during 2013 with 59 453 injured, 3 110 fatalities with a fatality of 3.03 %. In 2014 101 104 traffic accidents were reported with 58 148 injured, 2 798 fatalities and a lethality of 2.77 %. In 2015 95 532 traffic accidents were reported, 56 499 injured, 2 965 fatalities and a lethality of 3.10 %. During 2016, 89 304 traffic accidents were reported, 53 776 injured, 2 696 fatalities and a fatality rate of 3.02 %. In 2017, 88168 traffic accidents were reported, 53 776 injured, 2 826 fatalities and a fatality rate of 3.21 %. 90 056 traffic accidents were reported in 2018, 61 512 injured, 3 244 fatalities and a fatality of 3.60 %.

The statistical assessment of the traffic situation based on sample data of traffic accidents in the urban agglomeration

The article analyzes the accident rate on the roads at the entrance sections of the Belgorod agglomeration road network with the help of a documentary study of traffic accident records for the statistical assessment of the road traffic situation. It has been established that collisions and hitting a pedestrian are particular types of traffic accidents. The results of the analysis showed that Tuesday and Sunday are among the peak days of the week in terms of the probability of traffic accidents. Calculations based on mathematical statistics and probability theory have shown that there is a correlation between the number of accidents and the proposed characteristic hours of the day. To improve road safety, an information model was developed, an information model for reducing traffic accidents in places of concentration of traffic accidents.

РАЗРАБОТКА МАТЕМАТИЧЕСКОЙ МОДЕЛИ УПРАВЛЕНИЯ ДВИЖЕНИЕМ ТРАНСПОРТНОГО ПОТОКА

The paper analyzes the statistics of traffic accidents in the urban agglomeration. Sections of the road network with repeated types of traffic accidents have been identified. The issues of development of the theory of modeling of transport flows are considered. A fuzzy inference model based on the basic data of the traffic situation has been developed. A rule base has been formulated that allows determining the speed of traffic flow depending on the parameters of the traffic flow and the traffic situation.

The impact of politics, security and health on the escalation of road traffic accidents and their consequences in Iraq for the period 2015-2020.

This study aimed to detect the impact of politics, security and health on the escalation of road traffic accidents (RTAs) and their consequences in Iraq for the period 2015-2020. The data of this cross-sectional study were obtained from the annual reports of RTA statistics from the Central Statistical Organisation of the Iraqi Ministry of Planning. The statistical analysis of data was done using the Statistical Package for the Social Sciences. In this study, it was noted that the rate of RTAs, injuries and deaths increased from 16% to 19.5%, from 16.5% to 17.3% and from 16.2% to 20%, respectively, for the years 2015-2019, while the rates of RTAs, injuries and deaths in 2020 recorded a clear decrease compared to the rest of the years under study, and the rates were as follows: 14.8%, 14.4% and 14.1%, respectively. According to the types of traffic accidents, the rate of crashes recorded the highest rate of 51% in all years of the study compared to the run over, overturn and other unclassified accidents, which were 37%, 10% and 2%, respectively, and it was revealed that crashes, run over and other unclassified incidents increased from 5% to 20%, from 17% to 19% and from 18% to 26%, respectively, for the years 2015-2019, whereas all types of RTAs in 2020 were recorded the lowest rates compared to other years under study. Al-Basra, Baghdad, Al-Najaf and Babylon were recorded the highest rates of traffic accidents for the 6 years under study, with a rate of 13.3%, 11.7%, 11.1% and 10.4%, respectively. Although traffic accidents were not recorded in the years 2015, 2016 and 2017 for the governorates of Nineveh and Anbar due to the occupation of the terrorist Islamic State, traffic accidents maintained their levels due to internal migration. It was also noted that the rate of road accidents was the highest in 2019 compared to the years under study due to the liberation of occupied cities, the return of safety and the return of recording accident data again, while the year 2020 recorded a clear decrease in the rate of road accidents, especially in the first half of it due to the October protests that led to the suspension of all government institutions, universities and schools. The second reason for the decrease in the second half of 2020 is attributed to the lockdown of corona. It seems that the political, security, military and health factors have an effective impact on the traffic reality.

Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population

IntroductionThis study aimed to explore the trend and main influencing factors of road traffic accidents in Guangzhou, China, from 2007 to 2020 and to provide a reference and guidance for government decision-making. MethodsA retrospective cohort study was used to describe road traffic accidents in Guangzhou. According to the population types, all people with road traffic accidents were divided into migrant workers and the control population. We divided road users, administrative districts, motorcycle types and injury levels into subgroups to investigate the characteristics of road traffic accidents in Guangzhou. The road traffic accident data were derived from the Guangzhou Public Security Traffic Management Integrated System. ResultsThe incidence rate of road traffic accidents per 10,000 vehicles in Guangzhou decreased from 36.55 in 2007 to 10.07 in 2012, remained relatively stable at 9.47 in 2017, and finally rose to 11.12 in 2020. The injury rate showed the same trend as the incidence rate, while the mortality rate gradually decreased from 14.21 in 2007 to 5.19 in 2020. Vulnerable road users such as motorized two-to-three-wheeler drivers and migrant workers were casualties in more than 80% of the cases. The proportion of casualties involving mopeds and electric bicycles increased rapidly after 2018. Motor vehicle drivers frequently caused road traffic accidents and were most often uninjured. ConclusionRoad safety in Guangzhou has shown a clear trend of improvement, but casualties are uneven across administrative districts. More attention should be given to motorized two-to-three-wheelers, migrant workers, and road traffic violations by uninjured individuals.

농업기계 교통사고 예방을 위한 안전반사장치 설치 기준 설계 연구

Objective: In recent years, the risk of traffic accidents related to the agricultural machine is increasing along with the spread of urban complex districts. Therefore, this study was conducted to design standards for installing safety reflectors as part of preventing agricultural machine traffic accidents.BRBRBackground: The damage to life resulting from traffic accidents related to the agricultural machine is more serious than that of other types of vehicles, so it is urgent to prepare safety measures for it. Although night reflectors are used for the traffic safety of agricultural machine, there are limitations in securing its safety due to the problems associated with their visibility or attachment as the installation standards are ambiguous. Accordingly, to utilize safety reflectors efficiently, it is required to present standardized criteria for the installation of safety reflectors equipped with visibility.BRBRMethod: This study has examined the systems and specifications related to safety reflectors in Korea and overseas, analyzed the types of traffic accidents resulting from agricultural machine and investigated the status of attaching safety reflectors to agricultural machine, and derived implications for designing safety reflectors suitably for agricultural machine. Based on that, this study has presented the standards for installing safety reflectors and investigated their applicability to agricultural machine.BRBRResults: According to the findings, in foreign countries, the installation standards applied to the safety reflectors of agricultural machine are equivalent to those for automobiles, but in Korea, as no adequate standards are set yet, it is hard to apply them in reality. Therefore, this study suggests a draft of standards for three safety reflectors (the safety reflector plate sheet, rear mark of low-speed vehicles, & safety reflector band) and standards for installation. Also, according to the results of investigating the suitability of the installation criteria proposed, they have been found to be applicable.BRBRConclusion: This researcher performed fundamental research for the efficient use of safety reflectors suitable for agricultural machine. In follow-up research that takes the characteristics of various agricultural machines into consideration, this author will conduct additional experiments to secure more detailed installation standards and visibility.BRBRApplication: It is expected that the results of this study will be used as basic data to standardize safety reflectors and provide institutional support for the traffic safety of agricultural machines afterward.

Characteristics of injuries in cyclists injured when other vehicles collide with the side of the body of moving bicycles

BACKGROUND: Bicycle injuries, as a separate type of traffic accidents, require forensic medical examinations to establish the mechanism, duration, and severity of injuries. Forensic medical aspects of cycling injuries are insufficiently covered in the literature.
 AIM: To study the nature and features of the injury formation in cyclists who sustained injured when other vehicles hit the side of the moving bicycle.
 MATERIALS AND METHODS: The results of forensic medical examinations of 51 cyclists who died when the moving bicycles collided with the side of vehicles.
 RESULTS: The most frequently observed injuries in cyclists are traumatic brain injury (21.56%) and combined injuries, namely, head and chest (15.68%), lower extremities with trauma to the head, and chest (11.76%). Nearly all deceased cyclists sustained damage to the head (96.0%), which was characterized by the formation of a severe contusion of the brain with fractures of the parietotemporal and occipital bones. Damage to the chest and chest cavity organ was also observed frequently (50.69%). Rib fractures were noted in 74.2% of cases. Injuries to the abdominal organs were detected in 20.15% of the cases, such as liver rupture and hemorrhage in the ligaments of organs. Diaphyseal comminuted fractures of the bones of the lower leg and femur were also observed.
 CONCLUSION: The most common types of injuries in deceased cyclists in a side collision of other vehicles with a moving bicycle are traumatic brain injury and combined injuries of the head, chest, and fractures of the bones of the lower extremities in combination with a head and chest injury. Damage to the head was characterized by the formation of linear, depressed, and comminuted fractures of the parietaltemporal and occipital bones. Damage to the chest was characterized by fractures of the upper ribs and bruises of the lungs. Damage to the skin, resembling a picture of a road rash, was noted on the anterolateral body parts.

Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model

Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs.

Comparative analysis of AEB effectiveness based on typical and atypical scenarios of electric two-wheeler accidents in China

Electric two-wheeler (ETW) accidents are one of the most important types of traffic accidents in China. Currently, the China New Car Assessment Program (C-NCAP) has proposed test scenarios to evaluate the effectiveness of autonomous emergency braking (AEB) for ETWs, including several common typical crash scenarios, but other atypical scenarios have not been considered. To determine the performance of AEB in real accidents, 16 in-depth accident cases with typical scenarios and 11 cases with atypical scenarios were selected based on a proposed C-NCAP typical scenario set and reconstructed using the virtual simulation tools MADYMO and PC-Crash. The crashes were re-simulated with a car equipped with an AEB system while varying the sensor field of view (FOV), time-to-collision (TTC), sensor delay time (SDT), and lateral trigger width ( W). The results show that for almost all combinations of AEB parameters, the crash avoidance rate was much higher in the typical scenario than that in the atypical scenario. When using an AEB with a FOV of 90° (±45°), all ETW accidents were avoided in typical scenarios, while even with the most efficient AEB system (FOV = ±60°, TTC trigger value = 1.5 s, SDT = 0.1 s), only 82% of crashes were avoided in atypical scenarios. Further considering the effect of lateral width, increasing the width from 2 to 5 m, the maximum avoidance rate of the AEB system increased by 43% in the typical scenarios and 18% in the atypical scenarios. The findings suggest that the typical AEB test scenarios proposed for C-NCAP were useful for other crash scenarios, but that including additional test scenarios may better reflect real world crash scenarios. It is recommended that atypical scenarios should be considered in C-NCAP, particularly perpendicular crash scenarios with the car or ETW turning, to better describe real accidents and improve vehicle safety.

Specifics of the Criminalistic Description of Provoked Traffic Crimes

The investigation of traffic accidents within the framework of the crime under Art. 264 of the Criminal Code of the Russian Federation is, in most cases, a complicated and labor-intensive process primarily connected with the establishment and further investigation of the traffic accident’s mechanism. The investigation of a “classic” type of accident, in spite of the availability of investigation techniques, is a rather complicated process, while the investigation of “non-classic” types of traffic accidents poses legal challenges for the investigator acting as a law enforcer. This is explained by the absence of the investigation-court practice and investigation techniques of other types of traffic accidents, such as “no-contact” traffic accidents. The technique of investigating specific types of crimes has a great importance for a complete and comprehensive investigation of the analyzed type of accidents. By determining the typical features of preparing, committing and concealing the crimes, the criminalistic description contributes to the development of the most effective tactics, recommendations and necessary technical means of solving crimes.

Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network

The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.

The status of all types of rollover crashes in Northern Iran and the contributing factors

IntroductionThere is little information about this type of traffic accident in northern Iran.AimThis study aimed to determine the status of rollover crashes and the factors relating them.Material and methodsThis was a retrospective analytical study. The data of this study were obtained from the reports of Traffic Police and the Department of Forensic Medicine. The data of this study included related data on vehicle damages, injuries, and fatalities of 5999 rollover crashes of motor and non-motor vehicles. All analyses were performed with Excel and SPSS v. 21 software.Results and discussionThe probability of mortality due to rollover crashes decreases on slippery (<i>P</i> < 0.001) with age (<i>P</i> < 0.001) and at night (<i>P</i> < 0.001) and increases on road curves (<i>P</i> = 0.005) . The probability of injuries reduced with increase with age (<i>P</i> < 0.000) and in men (<i>P</i> < 0.027), on slippery roads (<i>P</i> < 0.04), heavy vehicle (<i>P</i> < 0.001), car (<i>P</i> < 0.009), pickup trucks (<i>P</i> = 0.011), but increased on the road curves (<i>P</i> < 0.001).ConclusionsCountermeasures include training drivers to increase their ability to control the vehicle and pay attention to the front and avoid driving in the opposite direction at the speed limit. Moreover, installing lower speed limits and installing speed cameras in high-risk areas reduce crashes. Corrective changes to the road and reduce road curve sharpness are as effective as trying to change drivers' behavior.

A Study on Disabling Injuries Prediction of Taiwan Occupational Disaster with Grey Rolling Model

In order to protect the safety and health of laborers and to achieve the goal of zero occupational accidents at work, the study takes the top three industries with the highest number of laborers inspections from 2010 to 2019, namely, construction, manufacturing, wholesale, and retail as the research object. Using three major indicators of disability injury including Disabling Frequency Rate, Disabling Severity Rate, and Frequency Severity Indicator as parameters, it applies grey theory to establish a GM (1,1) rolling forecast model. It further predicts the trend of disability injuries from 2020 to 2025. Based on the optimized GM (1,1) rolling model, the results show that there has the highest accuracy rate in the prediction of Disabling Frequency Rate (accuracy is 95.235% in K7) in construction. Disabling Severity Rate and Frequency Severity Indicator are both in wholesale and retail industries (accuracy is 97.044% in K6 and accuracy is 99.906% in K5). Therefore, Disabling Severity Rate has an upward trend, which is due to the common type of traffic accidents in the wholesale and retail industry. The study further proposes that relevant actual disaster cases could be the training materials and strengthen the communication in education to improve workers’ safety awareness for occupational disaster prevention.