Accident Research Research Articles

Trends and Patterns of Road Traffic Accidents in Maharashtra: A Comparative Examination of NCRB and MEMS Databases

Abstract Background: The emergence of high incidence of Road Traffic Accidents (RTA) has contributed to the burden of Non-Communicable Disease (NCD) both in terms of mortality and morbidity. It is very important to have a structured and accurate database for strategizing road safety and associated public health benefits. There are multiple sources of data available for the research of road traffic accidents and road safety. It is utmost necessary to identify the most appropriate data source for RTA research. Materials and Methods: The current study is based on five years’ time series data (2017 to 2021) from two data sources namely National Crime Record Bureau (NCRB) and Maharashtra Emergency Medical Services (MEMS). Injury rate and death rate was used along with the trend analysis to define the trend of RTA. Corelation coefficient was used for establishing the patterns of reporting of RTA in the state of Maharashtra. The data is analysed by using MS Excel and SPSS16.0. Results: Both the databases exhibit a declining trend in the reporting of RTA in the state of Maharashtra with a CAGR of -1.99% in NCRB and -13.77% in MEMS. Although the number of incidents reported in NCRB is around 40% less than MEMS, both the sources have almost the same number of victims per accident (r= 84%). Injury rate reported in NCRB is around 40% less than the MEMS data and the variation in injury rate in cities is high as per NCRB data than in MEMS data. MEMS data provides a number of information including the severity of injury which may be useful for the analysis and insight generation. Conclusion: Both the databases are important in providing crucial information for generating insight and strategies for road safety. A central database in combining both the database would be ideal for the researchers in the areas of injury and road safety. MEMS was the better source in terms of amount of information for epidemiological research in road safety.

Riding Through the Pandemic: Unveiling Motorcycle Crash Trends Amidst Three Years of the COVID‐19 Crisis

Bangladesh has a significant prevalence of motorcycle usage accompanied by a correspondingly high incidence of motorcycle‐related fatalities. The COVID‐19 crisis has brought additional challenges to road safety in Bangladesh because of containment strategies and restrictions. The impacts of the pandemic on motorcycle‐related road traffic crashes, injuries, and fatalities in Bangladesh are investigated in this study using ARIMA time series analysis. Data spanning 86 months (January 2016 to February 2023) were collected from the Accident Research Institute (ARI), which compiles newspaper‐based data serving as an alternative source of information on crashes encompassing both pre‐COVID (January 2016 to February 2020) and COVID‐19 periods (March 2020 to February 2023). Three COVID‐19 waves were demonstrated, with the first wave showing a significant decrease in crashes, injuries, and fatalities due to a government‐imposed lockdown. During the second wave, crashes and fatalities approached predicted values, while injuries remained lower than anticipated. The third wave witnessed a sudden drop, followed by a sharp rise in all three variables. Box and whisker plot analysis confirmed the disparities between observed and predicted values, with observed data being lower. These results demonstrate the significant impact that COVID‐19 containment strategies have had on trends in motorcycle crashes. By understanding these patterns, policymakers and road safety authorities can develop adaptive interventions to mitigate motorcycle‐related incidents during pandemics or similar crises. The study provides a data‐driven foundation for designing context‐specific policies, adjusting law enforcement strategies, and efficiently allocating resources to enhance road safety under varying conditions.

Research on Road Rage Detection System Based on Multi-feature Fusion

The influence of driver's rage on driving safety is one of the centers of traffic accident research. Road rage is a common potential factor affecting driving safety. Real-time monitoring of driver's emotional state and timely intervention measures can effectively reduce the incidence of traffic accidents. At present, the reliability of driver emotion recognition based on single consistent factor for road rage needs to be further improved. Therefore, based on the multi-feature fusion method, this paper proposes a recognition method of driver's road rage emotional state that integrates facial expression, driving control, voice text and physiological characteristics, and proposes the analysis of fusion strategy. In order to improve the reliability and implementability of road rage symptom judgment, this paper studies the state identification methods of road rage and integrates the characteristics of road rage. The focus of this study is to solve the key problem of insufficient reliability of road rage recognition based on facial expressions through the combination of multiple features and multiple data, aiming at the defects of the study on the single feature of road rage, and effectively improve the accuracy of road rage state emotion detection.

Biogas plants accidents: Analyzing occurrence, severity, and associations between 1990 and 2023

Biogas plants numbers are increasing worldwide, but their safety record is rarely investigated. This paper analyzes 75 occurrences of various types of accidents in biogas plants worldwide between 1990 and 2023. The study comprehensively reviewed accident reports and research literature with input from plant operators and safety experts. We aim to identify the common causes and consequences of accidents (occurrences) and suggest preventive measures to improve safety. The occurrences’ primary causes were component failure > maintenance error > natural and technological disasters (NaTech) > equipment failure > operational error > no personal protective equipment (PPE). The most common occurrences were gas explosions 69.3%, toxic gas releases (biohazard) 21.3%, asphyxia (biohazard) 4%, malfunctioning (electric and mechanical hazard) 2.7%, and fires 2.7%. The accident consequences ranged from minor injuries (76) to fatalities (51) and extensive property damage. Lack of PPE and gas pipelines (mechanical and biohazards) correlated positively and significantly (R2 = 0.70), while operational errors and asphyxia (biohazard) scenarios correlated positively and moderately (R2 = 0.55). The plant design, operating procedures, and maintenance practices strongly influence the occurrences’ likelihood and severity. This study provides valuable insights for stakeholders, researchers, and policymakers interested in promoting biogas’ safe and sustainable development. Future studies should investigate the relationship between plant size and accident frequency and assess the effectiveness of safety management and risk assessment methodologies in mitigating such occurrences.

Interobserver variability in preclinical assessment of collision variables following traffic accidents

PurposeTraffic accidents persist as a leading cause of death. European law mandates the integration of automatic emergency call systems (eCall). Our project focuses on an automated injury prediction device for car accidents, correlating technical and epidemiological input data, such as age, gender, seating position, impact on the passenger compartment, seatbelt usage, impact direction, EES, vehicle class, and airbag deployment. This study aims to explore interobserver variability in data collection quality in real accident scenarios. The assessment will evaluate the impact of user training and measure the time needed for data collection to inform user recommendations for the prehospital assessment.Insights from this study can aid in evaluating the ability of different professional groups to identify potential accident-independent parameters at accident scenes. This includes, among other things, relaying information to dispatchers at rescue control centers, also within the context of telemedicine approaches.MethodsDuring group sessions, real accident scenarios were presented both before and after a training presentation. Participants, including laypersons, accident research staff, emergency services, hospital physicians, and emergency physicians, visually assessed injury prediction parameters within a time limit. Training involved defining and explaining parameters using accident images. The study analyzed participant demographics, prediction accuracy, and time required, comparing assessment quality between professional groups and before and after training.ResultsIn summary, the study demonstrates that training had a significantly positive impact on the quality of assessment for technical accident parameters. The processing time decreased significantly after training. A notable training effect was observed, particularly for the parameters of rigid collision object, affected passenger compartment, energy equivalent speed (EES), and front and side airbags. It was recommended that individuals without prior knowledge should receive training on assessing EES. Overall, it was evident that technical parameters following a traffic accident can be well assessed through training, irrespective of the professional group.ConclusionSignificant differences in the assessment quality of technical accident parameters were observed based on technical and medical expertise. After user training, interdisciplinary differences were reconciled, and all professional groups yielded comparable results, indicating that training can enhance the assessment abilities of all participants in the rescue chain, while the time required for assessing accident parameters was significantly reduced with training.

MELCOR – DAKOTA coupling for uncertainty analyses in the SNAP environment/architecture

Uncertainty estimation to assess figures of merit characterizing evolution of a severe accident transient is a topic of current investigation in development of best-estimate plus uncertainty methodology. The probabilistic method to propagate input uncertainty is one of the methodologies used to develop Uncertainty Analyses (UAs). Using this methodology, UAs are performed by sampling probability distributions that describe the range of possible values that computer simulation model inputs can have. For each sample (or realization) of a set of uncertain input parameters, a computer simulation is performed. From the range of code simulation results obtained for each input realization, a distribution of code results is obtained. In this process, the distribution of input uncertainties is propagated to obtain a distribution of possible code results (i.e., the code output uncertainty). This probabilistic methodology is facilitated using Uncertainty Tools (UTs), which can be coupled with the accident analysis computer code to perform an UA. One of the UTs currently available is DAKOTA, developed by Sandia National Laboratories. DAKOTA is also provided as a SNAP plug-in. SNAP is a graphical user interface designed to support the use of USNRC codes (MELCOR, TRACE, etc). This paper is entirely derived from the NUREG/IA-532 issued by USNRC in April 2023 (Mascari et al., 2023) and has as a major target to summarize the main needs of UA in severe accident, the main element of the probabilistic method to propagate input uncertainty, and the workflow within SNAP to assist other interested analysts with their applications given they are members of the USNRC Cooperative Severe Accident Research Program (CSARP).

BIBLIOMETRIC ANALYSIS OF THE GREATEST NUCLEAR DISASTERS: WHAT IS KNOWN SO FAR AND WHAT ARE THE PROSPECTS?

Abstract. The high potential of nuclear power to combat climate change and support sustainable development plays a critical role in achieving the goal of the Paris Agreement. Moreover, with the ever-increasing demand for electricity, it is essential to consider nuclear power as a reliable source of energy. However, it should be noted that although the risk of a radiological accident is small, it always exists, and therefore it is crucial to have a complete understanding of the management of nuclear power. Herein, we present a statistical review of nuclear accident research at Chernobyl and Fukushima Nuclear Power Plants using bibliometric analysis to identify key patterns in scientific results and current issues in accident research. More than 10,000 articles have been collected from the SCOPUS database covering the periods 1986–2022 and 2011–2022 on Chernobyl and Fukushima, respectively. The results were obtained from two perspectives: first, we identified stages through which pre- and post-accident researches have evolved, and then we analysed the spatial correlation between energy-economic performance and scientific literature for the leading productive countries. From the analysis of research trends, it was found that the number of articles increased sharply immediately after the accident and slightly decreased over time. Among the most attractive subject categories in terms of the largest number of publications and received citations were environmental sciences, medicine and energy policy. The productive country ranking was mostly topped by the country in which the accident occurred. Spatial statistical analysis revealed a strong correlation between the scientific productivity and energy-economic performance of the leading countries.

Exploring the injury severity of vulnerable road users to truck crashes by ensemble learning

Large numbers of vulnerable road users were killed in truck crashes. In this study, ensemble machine learning models are constructed to predict the injury severity of the vulnerable road user (VRU) to truck (VRU-T) crashes. The study is based on the five years (2017–2021) of VRU-T crash data in the Shandong Province from the Center for Accident Research in Zibo. The injury severity of VRUs is estimated using machine learning ensemble models- Stacking, Voting, Random Forest, and eXtreme Gradient Boosting (XGBoost). Compared to the other three models, the Stacking has excellent predictive performance on the pedestrian and non-motorized datasets. Then, SHapley Additive exPlanations and Partial Dependence Plot box are introduced to analyze risk factors qualitatively and quantitatively. The innovative findings of this study are as follows: (1) as VRUs age, they are more likely to be seriously injured in truck crashes; (2) middle-aged truck drivers and truck drivers with medium driving experience increase the probability of VRUs being severe and fatally injured in truck crashes; (3) crashes involving heavy trucks, under signalized crossing, or on the national and provincial road and urban road have a positive effect on the crash severity for cyclists, and E-Bike riders.

Reconstruction of the Motion of Traffic Accident Vehicle in the Vehicle‐Mounted Video Based on Direct Linear Transform

Based on the principle of direct linear transformation (DLT) in close‐range photogrammetry, a method was proposed for reconstructing the motion states of the host vehicle and other vehicles based on vehicle‐mounted videos. To verify the effectiveness and accuracy of the method, validation experiments were designed. Under two typical operating states, steering and straight driving, the motion states of the host vehicle and other vehicles (including trajectory, distance, speed, and acceleration) were reconstructed from the vehicle‐mounted video. In the experiments, high‐precision inertial navigation was installed on the other vehicle to record real‐time motion data of the vehicle. Finally, in order to compare and analyze the reconstructed video results with the vehicle’s actual motion data, the recorded motion data were matched and synchronized to the same time axis as the vehicle‐mounted videos through a GPS timing device. The experimental result shows that the reconstructed trajectory results based on this method can generally reflect the vehicle’s actual trajectory, with an average deviation of less than 7.4%; the reconstructed distance results have an average deviation of less than 9.3%; the reconstructed speed results have an average deviation of less than 7.3%; the reconstructed acceleration results can reflect the vehicle’s acceleration or deceleration states. The results of this study provide an effective solution for obtaining important parameters of vehicles in accident reconstruction research, such as the trajectory, speed, distance, and acceleration or deceleration, and it has significant practical value for applications.

Pemodelan Kondisi Geometrik Jalan Terhadap Potensi Kecelakaan Lalu Lintas

Road infrastructure has an impact on the occurrence of road traffic accidents. The geometry of a road is a variable that influences the occurrence of road traffic accidents. Interpreting the observed crashes into an equation that can describe the crashes is one of the most difficult problems faced by scientists and engineers in any research. The traffic accident research conducted is still limited to analyzing the causes of accidents and jumping to handling strategies that do not reveal how much geometric influence on accidents at the location. This research was developed using a comprehensive modeling approach to illustrate how highway geometric design, in this case the alignment, influences crash rates. This study aims to analyze the influence of road geometrics on the incidence of accidents. The method used is multiple liner regression measurement model using R-Studio. The variables to be studied include road geometry planning parameters, especially horizontal alignments which include stopping sight distance (X1), radius of horizontal curve (X2), shoulder width X3, and transition curve length (X4). The results of the study obtained a relationship in the mathematical equation Y = 99.55508-0.02634X1+1.17647X2-0.06999X3-0.03906X4 with the most significant correlation value produced is the transition curve length and radius of horizontal curve. Based on the multiple R-squareed value of 0.8775 this model has a very strong correlation category. Where the ability of X1 to X4 is able to explain Y by 80%.

Optimization of prevention and mitigation of severe accidents through Research: Major outcomes and outlook of the Spanish programme

Severe accidents research activities in Spain have grown substantially since the Fukushima-Daiichi accident. Most of these research activities have been conducting by a few organizations (national research center and academia) with the support of the Regulatory Authority, utilities and engineering companies. A great number of these activities were conducted in the analytical field and their main outcomes are contributing to a better understanding of severe accident phenomena (i.e., pool scrubbing; combustible gas distribution and combustion; fission products transport; etc.) and the efficiency of severe accident management actions (i.e., FLEX; PARs; activation of safety systems, long-term measures; etc.). The national framework set between researchers and end-users (regulators and utilities) is improving the safety of the Spanish nuclear power plants and allows for the assimilation of the research results by the nuclear industry.

Knowledge graph construction and visualization analysis of coal mine accident safety management research based on CiteSpace and VOSviewer

Although the coal mining industry has implemented mechanization and automation, frequent coal mine accidents still occur, making coal mine safety a primary concern. To gain a comprehensive understanding of global progress in preventing coal mine accidents and managing safety, we utilized the Web of Science database (WOS) as our sample source. With the support of CiteSpace and VOSviewer software, we constructed a knowledge graph depicting publication numbers, research institutions, and keyword clustering to visually analyze coal mine accident prevention and safety management. Our research reveals that this field has undergone three stages of development: initial growth, steady progress, and rapid advancement. Particularly noteworthy is the current golden age of rapid development from 2018 to 2023 which has garnered significant attention from researchers. In terms of author collaboration networks, notable contributors with high publication rates and citation counts include Wu Qiang, Liu Quanlong, et al. Regarding institutional cooperation networks, major research institutions such as China University of Mining & Technology, Shandong University of Science & Technology, and Anhui University are actively engaged in important scientific research related to coal mining. Through keyword clustering analysis based on relevant literature review, it is evident that research on coal mine safety management primarily focuses on high-frequency keywords such as "coal and gas outburst","risk assessment" and "coal miners" By extracting the top nine emergent keywords with high-intensity levels and arranging them chronologically according to their emergence year, the evolution pattern over time was obtained for emergent words. Furthermore, it was observed that emerging keywords like "China", "subsidence" and "workers" have also gained increasing attention during this period.

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents.

Critical review on data-driven approaches for learning from accidents: Comparative analysis and future research

Data-driven intelligent technologies are promoting a disruptive digital transformation of human society. Industrial accident prevention is also amid this change. Although many emerging technologies, such as machine learning (ML), are extensively employed in workplace safety, these approaches need to fit the intended safety purpose of accident analysis, risk assessment, adverse outcome prediction, or anomaly detection. Hence, examining the “real-world” need for accident prevention and the advantages of emerging data-driven methodologies to better integrate them is necessary. This study provides a systematic review to clarify the current research status, existing problems, and future insights into these evolving technologies in accident prevention. We present notable gaps and barriers in data-driven accident prevention by analyzing 194 published studies from four perspectives: Paradigm, Model, Data Source, and Purpose. The results demonstrate (1) lack of a systematic framework to guide the application of Big Data (BD) in the field of safety; (2) few prior studies have considered model interpretability; (3) more proactive data needs to be incorporated into accident analysis; (4) safety-related data and domain knowledge need to be further integrated; (5) some recent data-driven techniques are unexplored in safety science. Further, the future research opportunities are discussed based on these findings. Such review may help clarify the mapping of data-driven tasks to safety goals to accelerate the uptake of data-driven technologies in safety or accident analysis research.

Cold Crucible Facility for Severe Accident Research at Canadian Nuclear Laboratories

Abstract The properties and behavior of corium are important factors in the progression and ultimate consequences of a severe nuclear reactor accident. Canadian Nuclear Laboratories (CNL) is developing a cold crucible facility to create and study small quantities of CANDU corium and, potentially, other molten materials. This paper describes the facility and presents preliminary results with nonradioactive charges and with radioactive, near-prototypical charges.

Quality evaluation tool for animal experiments and the usage specifications

The animal experiment is the best method for therapeutic efficacy testing of various treatment methods before clinical application and serves as an irreplaceable “live reagent” and “live precise instrument,” thus playing a vital role in biomedical science research. However, there were significant differences between the results of animal experiments and clinical efficacy in previous findings that are hard to ignore, especially in cerebrovascular accident research due to the unsatisfactory specification of animal experiments. Accordingly, more prescriptive and standard tools are required for evaluating the quality of animal experiments. This study will introduce the current commonly used quality evaluation tools for animal experiments.

Causation analysis of seafarers’ unsafe acts: An investigation on individual features and safety consciousness

Studies on the effect of individual attributes and the internal influence of organizational management on individuals are important to enrich maritime accident research dominated by organizational perspective and to make such accident intervention management pertinent. The theory of planned behavior (TPB), which is well applied in road traffic studies and other fields, is introduced to investigate the individual-related causes of seafarers' unsafe acts. A comprehensive survey is conducted and seafarers' personal and professional attributes, work attitude, safety consciousness, and tendencies under different navigation scenarios are collected. With these data, a structural equation model to reveal the causal mechanism of seafarers' unsafe acts is established from an individual perspective, especially with regard to the influence of the latent factors. The results are as follows: (1) the improvement of safety consciousness mitigates unsafe acts, whereas a high work attitude increases the possibility of their occurrence through weakened safety consciousness; (2) among personal and professional attributes, satisfactory family life, higher education, and longer job tenure are positively related to seafarers’ safety consciousness, whereas continuous work and a higher job position cast negative influence; and (3) more favorable environments make seafarers lower their vigilance that results in unsafe acts.

A methodology for crash investigation of motorcycle-cars collisions combining accident reconstruction, finite elements, and experimental tests

Motorcycle and in general motorized 2–3 wheelers accidents are a major concern of road safety worldwide representing about 28% of the overall fatalities. Speed is a central factor in the increase of injury severity of the accident’s intervenients. The main factors and causes of an accident can be determined with accident reconstruction being the reason for its popularity in accident safety research. A methodology of three different and complementary accident reconstruction methods is proposed and applied to a real accident where a motorcycle impacts the front side door of a passenger car. No other study in the literature presents a similar methodology for motorcycle accident reconstruction. With PC-Crash all accident dynamics were determined in the first place with a complete accident reconstruction. Afterwards, a finite element simulation of the impact between the motorcycle tire and the car’s door was conducted in Ansys LS-DYNA using all the results extracted from PC-Crash. The results of both these analyses correlated well with reality. To validate the finite element model, a pendulum experimental impact test was performed. It was achieved a good correlation from a kinematic point of view. An accurate accident reconstruction and a correct validation of the kinematics are possible with the proposed methodology. A good correlation of results between the three different methods was achieved. The proposed methodology enables for a more accurate estimation of the speed of motorcycles involved in accidents which is of extreme importance in accident investigation.

Current status of MELCOR 2.2 for fusion safety analyses

MELCOR is an integral code developed by Sandia National Laboratories (SNL) for the US Nuclear Regulatory Commission (USNRC) to perform severe accident analyses of Light Water Reactors (LWR). More recently, MELCOR capabilities are being extended also to analyze non-LWR fission technologies. Within the European MELCOR User Group (EMUG), organized in the framework of USNRC Cooperative Severe Accident Research Program (CSARP), an activity on the evaluation of the applicability of MELCOR 2.2 for fusion safety analyses has been launched and it has been coordinated by ENEA. The aim of the activity was to identify the physical models to be possibly implemented in MELCOR 2.2 necessary for fusion safety analyses, and to check if those models are already available in MELCOR 1.8.6 for fusion version, developed by Idaho National Laboratory (INL). From this activity, a list of modeling needs emerged from the safety analyses of fusion-related installations have been identified and described. Then, the importance of the various needs, intended as the priority for model implementation in the MELCOR 2.2 code, has been evaluated according to the technical expert judgement of the authors. In the present paper, the identified modeling needs are discussed. The ultimate goal would be to propose to have a single integrated MELCOR 2.2 code release capable to cover both fission and fusion applications.

Predictive Vehicle Safety—Validation Strategy of a Perception-Based Crash Severity Prediction Function

Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.