Accident Database Research Articles

An investigation of ADAS testing scenarios based on vehicle-to-powered two-wheeler accidents occurring in a county-level district in Hunan province

Abstract To meet the requirements for high-risk testing scenarios in the safety assessment of advanced driver assistance systems (ADAS), this study selected 239 vehicle-to-powered two-wheeler accidents occurring in Xiangtan County from the China In-depth Mobility Safety Study—Traffic Accident (CIMSS-TA) database. Seven scenario elements were selected from the levels of static road elements, dynamic traffic participants and environment elements. The accident characteristics were obtained through statistical analysis. The results showed 75.73% of accidents occurred at T-junctions/intersections, and 34.73% of accidents were caused by the visual obstructions. Six typical vehicle-to-powered two-wheeler crash scenarios were established by cluster analysis, including two straight road scenarios (scenario 3 and 6), two T-junction scenarios (scenario 1 and 5), and two intersection scenarios (scenario 2 and 4). Furthermore, six vehicle-to-powered two-wheeler testing scenarios, which represent the traffic environment characteristics of Chinese county-level districts, were constructed by analyzing the vehicle/PTW speed, the first collision point of vehicle, obstacle type and location, etc. The results showed the vehicle speed in scenario 4 was higher than that in scenario 2, with the average values of 34.32 km/h and 21.99 km/h, respectively. The results of this study provided testing scenarios for the safety assessment of ADAS, and contributed to improving the vehicle active safety technology.

Application of Machine Learning Modeling for the Upstream Oil and Gas Industry Injury Rate Prediction

Introduction: Yearly, the International Labor Organization report indicates many workplace accident occurrences. The degree of the happenings depends on the workplace environment setting and the incident regulatory measures implemented. By the nature of its work environment, the oil and gas upstream sector is susceptible to high incident rates. In the current fierce business competition and practices, improving productivity, quality, and other processes, such as Safety, is vital. Implementing well-designed safety procedures is the key to managing and reducing the risk level of workplace incidents. Methods: Recently, the application of Machine learning (ML) modeling for accident/injury prediction has been reported in the construction, mining, transport, and health sectors. Likewise, the objective of this paper was to implement three machine-learning-based models to predict injury rates in a drilling operation. The petroleum safety authority of Norway provided the datasets. First, the dataset was pre-processed, and then the selected features and target dataset were used for the modeling. Finally, the model prediction and performance accuracy analysis were performed. Results: Results showed that multivariable regression (MVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning algorithms-based models predict the test data with R2 values of 0.9576, 0.793, and 0.97036, respectively. Conclusion: As the common saying goes, 'prevention is better than cure.' For this, implementing methods such as improved work processes and Health, Safety, and Environment (HSE) mitigation procedures, workplace injuries, and accidents allow for reducing the risk level of workplace injuries. The application of integrated machine learning tools, along with carefully built-in workplace accident database implementation, will provide early detection and possible remedial precautions that can be taken to prevent workplace injuries/accidents/fatalities. However, extensive research and development are required to deploy the method in real life. Combining Machine Learning modeling and carefully designed safety measures is vital for successful and robust predictive tools.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009–2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 820 times more fatal/grievous injuries than simple injuries on expressways per 1000 crosswalk crashes (posted speed limit: 100 km per hour). Working pedestrian age group (25–64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

The influences of human factors on road accidents in Romania in the period 2013 – 2022.

The main objective of the study is to establish a correlation between the main causes of road traffic accidents in Romania and “human factors”. The aim of the study is to show that accidents are attributed exclusively to human mistakes in a higher percentage than. The data used in the research are taken from the database of the Traffic Police-for Road traffic accidents during 2013-2022 and from the database of the Romanian Road Authority - ARR - for road safety inspections during 2018-2021. From the perspective of human factors, the research will give answers on the nature of the causes that lead to the production / favouring of road accidents in Romania and will propose solutions to address road risks to improve road infrastructure safety. As a case study, the research will focus on the human factors that contribute to road accidents on National Road 2. Compared to other national roads, on DN 2 road accidents occur with the highest number of fatalities, approximately 8.28% of all road deaths nationwide. The analysis of findings from road safety inspection reports carried out on DN2 shows that there is a correlation between road infrastructure non-conformities and causes of road traffic accidents.

Analysis of bicycle crash characteristics and typical scenarios in France: A descriptive epidemiology and clustering approach

Bicycle crashes can result in severe injuries and fatalities, emphasizing the importance of identifying typical scenarios and characteristics of these crashes to develop effective prevention and mitigation strategies. This study analyzed datasets of bicycle crashes in France from 2005 to 2021 extracted from the French national road traffic accident database, evaluating bicycle accident characteristics through conventional descriptive epidemiology approaches and using the K-medoids clustering method to identify typical scenarios and their associated characteristics. Among 1,121,571 road traffic accidents in France, 80,851 were bicycle crashes, with a decline between 2005 and 2013 but a steady increase since then, peaking in 2020 at approximately 1.74 times that of 2005. Victims of bicycle crashes were primarily between 11 and 62 years old, with the largest population among 14–33 years old. Male victims were more involved in bicycle crashes than female victims, with the majority of crashes involving a moving vehicle. The majority of bicyclists involved in accidents were found not wearing helmets, and even bicyclists wearing helmets still sustained injuries. Typical bicycle crash scenarios included four bicycle-vehicle head-on-side collisions, one bicycle-obstacle collision, and two bicycle-pedestrian collisions, with bicycle-obstacle collisions often resulting in serious injuries similar to bicycle-vehicle collisions. Pedestrians were more likely to sustain injuries than bicyclists during bicycle-pedestrian collisions, and their injuries were often more severe than those of bicyclists. The study provides insights into the characteristics and typical scenarios of bicycle crashes in France and can be used to develop targeted interventions to improve road safety for cyclists.

Acute occupational inhalation injuries-United States, 2011-2022.

Inhalation injuries due to acute occupational exposures to chemicals are preventable. National surveillance of acute inhalation exposures is limited. This study identified the most common acute inhalation exposure-related incidents by industry sector among US workers. To characterize inhalation-related injuries and their exposures during April 2011-March 2022, state and federal records from the Occupational Safety and Health Administration (OSHA) Occupational Safety and Health Information System (OIS) accident database were analyzed. Industry-specific injury, hospitalization, and fatality rates were calculated. The most frequent acute inhalation incidents investigated by OSHA were caused by inorganic gases (52.9%) such as carbon monoxide (CO) or acids, bases, and oxidizing chemical agents (12.9%) such as anhydrous ammonia. The largest number of fatal and nonfatal injuries were reported in the manufacturing (28.6%) and construction (17.2%) sectors. Workers were affected by acute inhalation exposures in most industries. Using this surveillance, employers can recognize frequently-occurring preventable acute inhalation exposures by industry, such as inorganic gases in the manufacturing sector, and implement prevention measures. Training of workers on exposure characteristics and limits, adverse health effects, and use of protective equipment by exposure agent can prevent inhalation injuries.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data

Objective The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. Methods This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. Results The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. Conclusions Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study’s findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

Fires and fire risk perception predictors on Finnish farms

ABSTRACT The physical and operational farm environment has become more complex, challenging risk management. This study aimed to identify the number and typical features of farm fires and the factors connected to farmers’ assessments of fire risk. The data comprised a subset extracted from the national accident database of the Finnish rescue services from 2000 to 2021 (3,989 farm fire alarm calls) and farm survey data collected in the spring of 2021 (506 valid responses). The descriptive analysis of the fire incident data showed that the most typical fire sites on farms were dairy cattle buildings, grain dryers, and heating plants. Most farmers (83.4%) assessed the probability of fire as intermediate or high. The Bayesian machine learning approach used to analyse the survey data suggested the control of malfunctions, failures of machinery and equipment, and educational efforts were key elements in fire risk management.

System Resilience of a Liquid Hydrogen Terminal During Loading and Unloading Operations

Hydrogen technologies are playing an increasing role in transportation and industry. Key for taking maximum advantage out of the great gravimetric energy density of hydrogen are feasible and safe storage and transfer concepts. For the successful implementation of a hydrogen-based society with several green industrial initiatives, it is indispensable to develop an infrastructure of liquid hydrogen (LH2) terminals and tanker ships with the capability to bunker LH2. Liquid hydrogen might be used to store and transport large quantities of hydrogen. LH2 is a cryogenic fluid, so double-walled vacuum insulated tanks are required to keep it cold for long periods of time until further distribution or use. Whether transportation sector or industry, to date there is little knowledge on safety related issues available. Main concern when handling hydrogen are accidental releases that can lead to integrity damage on materials and structures, fires, and explosions. Assuming that LH2 infrastructures will be widely deployed and in use all over the globe, accidents are possible to occur during loading and unloading of LH2 bunkering facilities. Therefore, it is necessary to conduct a detailed risk assessment that focuses on system resilience to improve the capabilities of the facility to keep its functionality up when errors occur. This study refers to the LH2 storage tank installed in the LH2 terminal in Kobe, Japan. This stationary storage tank is an essential element of the terminal that was constructed by the Hydrogen Energy Supply Chain Technology Research Association (HySTRA). HySTRA aims at the distribution of hydrogen in liquified form by ship from Australia to Japan. To date, there is only little data available regarding the facility in Kobe. Nevertheless, due to the novelty of the technology the risk for accidents to occur might be higher than in conventional fuel distribution terminals. Accidents might happen due to technical failures, human errors, or external causes such as natural events. The consequences could be catastrophic. Some of these may expose structures and personnel to extreme low temperatures, fires, and explosions which may hinder bunkering operations of the facility in Kobe. This study gives an overview on possible scenarios that lead to loss of containment and provides an insight in the process of evaluating system resilience during such a scenario. This work details with a framework for assessing system resilience applied to LH2 storage facilities. The system resilience calculation involves the examination of a critical hydrogen accident database and provides suitable preventive and mitigative safety barriers.

The Assessment and Response of Rehabilitation Professionals to Sudden Deterioration in Symptoms: An Analysis of the National Database in Japan.

There are few analyses of the current status of and responses to acute deteriorations encountered by physiotherapists, occupational therapists, and speech-language pathologists (rehabilitation professions [RPs]). The purpose of this study was to analyze the responses of RPs to acute deterioration in patients using the functional resonance analysis method (FRAM) based on the descriptions in "the Medical Accident Database". Subjects were 413 cases with medical incidents reported by RPs to the database from 2012 to 2021. Life-threatening cases with changes in consciousness, circulation, and respiration were selected. Descriptions regarding findings assessed by RPs and support team, and requests for assistance were extracted. We also attempted to construct appropriate respond in RPs by using the FRAM. Thirty-nine cases of acute deterioration were included in the analysis, and descriptions by RPs of consciousness (35 cases), circulation (18 cases), and respiration (36 cases) were identified. Blood pressure and percutaneous oxygen saturation measurement were frequently presented in the assessment by RPs, whereas the support team assessed cardiac arrest and respiratory arrest in high frequency. The FRAM analysis indicated that appropriate and rapid post-response by RPs requires patient information in prior, appropriate assessment and integration/interpretation. We attempted to identify problems analyzing the response by RPs to acute deterioration using the database and construct an appropriate response model. It resulted that RPs need to obtain patient information in advance and integrate/interpret it appropriately based on accurate assessment of conscious, circulation and respiration for rapid response. A model including integration/interpretation for appropriate post-response by RPs was constructed using the FRAM.

A data-driven risk model for maritime casualty analysis: A global perspective

Maritime casualty analysis needs to be addressed given the increasing safety demand in the field due to the accidents’ low-frequency and high-consequence features. This paper aims to delve deeper into the factors that affect maritime accident casualties by establishing a new database and conducting an accident casualty evolution analysis. Based on the refined dataset, a pure data-driven Bayesian Network (BN) model is developed to conduct the casualty analysis of maritime accidents that occurred under different ship operational conditions. Methodologically, it introduces new risk factors to improve maritime casualty analysis accuracy through the enriched updated maritime accident database. Furthermore, the new database is categorised into five new datasets based on temporal development trends to better analyse the evolution of the casualty. Five risk analysis models are individually constructed based on different timeframes to illustrate the dynamics of the casualties and compared by seven evaluation indexes to demonstrate the effectiveness of the proposed data-driven BN model. It, for the first time, investigates the changing roles of different risk factors on maritime casualties with time. The insights gained from this model are invaluable, contributing to improved risk prediction and maritime safety strategies by acknowledging the changing patterns of maritime accidents.

Collection and analysis of hydrocarbon gas buried onshore pipeline accidents in Indonesia as the databases for failure frequency assessment in a quantitative risk analysis

AbstractAs of May 31, 2022, approximately 18,687 km of gas pipeline has been built in Indonesia. However, the pipeline failure data are yet to be gathered or evaluated. In quantitative risk assessment (QRA), the pipeline failure frequency is calculated by a generic failure frequency approach. Most of these generic failure frequency data are based on other region pipeline incident databases. This paper aims to provide the basis of data for the failure frequency of Indonesia's buried onshore pipeline. Accident data from 1127.9 km of underground onshore gas pipelines within 30 years (1976–2006) in Indonesia were gathered and assessed to provide the cause and effect of pipeline age and diameter on the generic failure rate. This research reveals the average failure rate of 5.32E‐04/km‐year of onshore pipelines in Indonesia. The study concluded that baseline failure frequencies from this analysis could be used for buried onshore pipeline quantitative risk assessment purposes. Further pipeline failure research for a relatively current period (e.g., from 2006 to 2022), type of installation (e.g., buried, or non‐buried), and other applications of the pipeline (e.g., offshore pipeline) should be conducted as a way forward for this study to have a complete database for Indonesian accidents.

The atypical transport system: Natural gas pipeline system

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

Çocukluk Çağı Ev Kazalarının Adli Tıbbi Değerlendirmesi: 2 Yıllık Retrospektif Tek Merkezli Bir Çalışma

Background: Children are at a significant risk of domestic accidents due to their interest, curiosity, and lack of awareness of their surroundings. This study aims to evaluate domestic accidents from a medicolegal perspective and draw attention to the frequency and severity of such accidents in childhood.&#x0D; &#x0D; Materials and Methods: All ICD-10 codes related to domestic accidents and injuries were scanned and reviewed. A total of 1,835 cases in the age group of 0-18 years, who presented to our hospital with a history of domestic accidents, were identified between January 1st, 2015, and December 31st, 2016.&#x0D; &#x0D; Results: The mean age of the cases was found to be 45 months (min: 0, max: 215). It was observed that domestic accidents occurred predominantly in the 13-24 months age group, accounting for 494 (26.9%) cases. The majority of accidents occurred in the living room, constituting 672 (36.6%) cases, and the most common type of accident was falling, accounting for 960 (52.4%) cases. Only 451 (24.6%) of the study population were reported to judicial authorities.&#x0D; &#x0D; Conclusion: In cases of domestic accidents, especially if they are repeated, to avoid overlooking child abuse cases, it is recommended to seek opinions from experienced forensic medicine practitioners in this field, if possible. Establishing a national domestic accident database and conducting further studies on domestic accidents in children will enable us to comprehend the true extent of these accidents and develop new and diverse approaches to prevent them.

Railroad accident analysis by machine learning and natural language processing

The evolving complexities of railroad systems also increase their vulnerability to failure from human error. This study compared the outcomes of two workflows that incorporated 11 different machine learning techniques to identify characteristics of railroad operations that are generally associated with human-caused accidents. The first workflow engineered features from the fixed attribute fields of a large railroad accident database and the second applied natural language processing to extract features from the unstructured accident narratives. Both workflows applied a Shapely game-theoretic model to rank the importance of features based on their marginal contribution towards predicting accident cause. Among several interesting findings, some of the most unexpected were that human-caused accidents are generally not associated with high train speeds nor derailment type accidents, and that shoving cars is riskier than pulling cars. Those, and other findings, from this study can inform management decisions, planning, and policies to minimize the risk of human-caused accidents.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Objective As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist’s driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes. Methods Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve. The classification and prediction performances of the six machine learning methods on the dataset are compared, and the LightGBM algorithm with the best performance is selected to model the accident injury severity of the motorcyclists. The SHAP method is used to extend the interpretability of the LightGBM model results. Based on the SHAP method, the importance, main effect, and the interaction effect of factors under each accident injury severity are quantitatively analyzed. Results The model prediction accuracy is 92.6%, the F1-Score is 92.8%, and the AUC value is 0.986. The importance of factors varies with the accident injury severity of motorcyclists. The kilometers traveled per year by the driver, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors. The motorcyclist is more likely to suffer fatal injuries when the throwing distance is >1,000 cm. Conclusions The prediction model of driver injury severity based on LightGBM algorithm has a good prediction performance. It can be used to analyze the influence factors of injury severity in two-wheeled motorcyclist accident by combining the model with SHAP method. These results could help the traffic management department to take measures to reduce accident injury of motorcyclists.

Analysis of the Severity and Cause and Effect of Occupational Accidents in South Korea

The industrial sector in South Korea has recently undergone significant growth; however, it is also known for its hazardous workplaces. Occupational accidents have had a widespread impact across various industries; therefore, the identification of accident-influencing factors is crucial to improve workplace safety. We analyzed the occupational accident database from the Ministry of Economy and Finance to examine the influencing factors, including worker information, project details, time-related variables, and accident descriptions. Exploratory and correspondence data analyses were performed to identify patterns and relationships between variables. We applied multinomial logistic models and random forest algorithms to understand the correlation between victim status and independent variables. Results showed that 67% of all accidents occurred among workers with less than one month of employment. The multinomial regression model achieved a prediction accuracy of 97.66% with a kappa value of 0.846, outperforming the random forest model (kappa = 0.844). The receiver operating curve illustrated that the random forest had higher misclassification rates when distinguishing between injuries and fatalities. To mitigate accidents among new workers, enhanced safety training and protective measures are needed to enforce a healthy workplace. This study contributes to ongoing efforts to advance workplace safety, reduce occupational accidents, and promote a healthier working environment.

Preliminary evaluation of a passive safety device concept for the protection of riders

Powered Two-Wheeler riders are over-represented in crash statistics worldwide, and currently available safety solutions have not triggered a decreasing trend. A new device concept, comprised of a wearable vest with belt stripes sewn on the back and fastened to the motorcycle frame through a metal cable, was developed to save riders’ lives. The objective of this paper is the evaluation of its protective effectiveness in a set of representative crash configurations selected from the ISO 13232 database. The configurations were reproduced in a multibody environment, and each of them was characterised through the following parameters: the impact speeds, contact points, relative heading angle, and the Boolean presence of the safety device. The preliminary evaluation revealed encouraging outcomes as several body regions reported reduced injury indices. However, the benefits depended on the speed range and impact configuration. Overall, the benefits outweighed the additional risks in the selected accident database.

Factor diagnosis and governance strategies of ship oil spill accidents based on formal concept analysis

Ship oil spill accidents have a prolonged duration, complex consequences, challenging cleaning and repairing efforts, and pose a significant threat to the environment, economy, and society. Eliminating irrelevant information and identifying key factors using traditional methods is challenging due to the complexity of the causes of ship oil spill accidents. To address this, this article sorts out the accident databases of the International Tanker Owners Pollution Federation (ITOPF) and eight national maritime administration agencies, and innovatively constructs a formal concept analysis (FCA) model based on reports of 100-plus ship oil spill accidents. The model results prove that improper operation, less complete ship equipment, large tonnage, and poor navigation conditions are the key factors. The different causal rules of oil spills in collision/contact, grounding, fire/explosion, and foundering are further compared and analyzed. Finally, corresponding improvement measures are put forward for the key factors of oil spills and different causal rules.

Revving Up Risk: The Surge of Superbikes in India and its Impact on Road Traffic Accidents

Abstract In 2021, India recorded over 412,000 accidents, causing nearly 154,000 deaths, with the 18-45 age groups being most affected. The Ministry of Road Transport and Highways has implemented various measures, including vehicle and road improvements, educational campaigns, and the 2019 Motor Vehicle (Amendment) Act. They’re also employing technology like Integrated Road Accident Database (iRAD) and e-Detailed Accident Report (eDAR) for real-time accident analysis. The subsequent section highlights the significant growth of the luxury motorcycle market in India. Factors driving this include rising disposable income, a shift in perception of superbikes as a lifestyle statement, expanded exposure through events and media, enhanced dealership networks, and customization options. The popularity of long-distance touring and adventure riding further fuels this trend. However, a disparity exists between the rise of superbikes and road development. Poor road conditions, traffic congestion, and a lack of safety measures hinder the riding experience. Additionally, limited access to remote areas and insufficient road safety education contribute to the problem. The anticipated issue is the escalating rate of road accidents, with vulnerable road users at heightened risk. The report also outlines common injuries from motorcycle accidents, ranging from head injuries to psychological trauma. The way forward advocates for cautious riding in India, given existing road conditions. Manufacturers are urged to promote road safety, provide insurance, and collaborate with organizations for road maintenance. Riders pursuing adventure or off-road experiences should prioritize safety by appropriately equipping their superbikes. At a policy level, initiatives like roadside warnings and cardiopulmonary resuscitation (CPR) training for traffic police can significantly enhance road safety. Overall, synchronized development between high-end superbikes and road infrastructure is crucial to curbing further casualties and disabilities in India.