Traffic Accidents Research Articles

Epidemiology of Penetrating Chest Injuries Presenting at a Tertiary Care Center in Peshawar: A Retrospective Study.

Trauma is the leading cause of mortality globally.Penetrating chest trauma is a type ofinjury that occurs when an object pierces the skin and enters the chest wall. The incidence of penetrating chest trauma is high in the Khyber Pakhtunkhwa Province of Pakistan, with firearm injuries being the most common cause. The objective of this study is to determine the epidemiology and outcome of non-cardiac penetrating chest injuries presented at a tertiary care center in Peshawar. This retrospective cohort study was conducted from January 2022 to January 2023 at Medical Teaching Institute, Lady Reading Hospital, Peshawar, KPK, Pakistan. Two hundred and three patients who suffered penetrating chest trauma in the Khyber Province of Pakistan between January 2022 and January 2023 were included in this retrospective cohort study. The epidemiology and outcomes were analyzed using IBMSPSSStatistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). The mean age of the patients was 30.25 ± 16.674 years. Males comprised 183 (90.1%) of the study sample whereas females comprised only one-tenth 20 (9.9%). Gunshot injuries were the predominant mode of penetrating thoracic trauma comprising 128 (63.05%) of all injuries. Injuries inflicted by knife constituted approximately one-fifth of the presentations 44 (21.67%). The remaining injuries labeled 'Others' comprised modes such as road traffic accidents and fall injuries where the predominant mechanism was penetrating injury. The latter comprised 31 (15.27%) of the injuries. Penetrating chest trauma is common in the Khyber Pakhtunkhwa Province of Pakistan. Gunshot injury is the most common mode. Males are predominantly affected. Most non-cardiac penetrating chest trauma can be managed conservatively.

Work-related road traffic accidents: emergence of new modes of personal journey – analysis based on data from a register of road traffic accidents in France

IntroductionAccording to the 2018–2019 People Mobility Survey, work-related journeys (commuting and on-duty journeys) account for approximately 25% of all journeys. The use of non-motorised (nm) and motorised (m) personal mobility...

A Study Evaluating the Functional Outcomes of Mason Type III and IV Radial Head Fractures Treated With a Radial Head Prosthesis.

Objective This research aimed to assess the functional result of type III and IV radial head fractures that were treated using a radial head prosthesis. Methods A retrospective investigation was conducted on 70 patients with type III and IV radial head and neck fractures, as classified by Mason. The patients were hospitalized and received treatment at the Orthopaedics Department, where they had radial head prosthesis surgery for three years. Results Among the total of 70 cases, 42 (60%) cases were below the age of 40, while 28 (47%) cases were over 40 years. The average age was 36.4 years. The maximum age recorded was 54 years, while the lowest age recorded was 30 years. The female population outnumbered the male population. The majority of instances (42, 60%) were attributed to falls, while the remaining cases were caused by road traffic accidents (RTAs). Out of the total 70 instances, 52 cases (74.28%) exhibited right-side dominance, whereas 18 cases (25.72%) exhibited left-side dominance. Within our case study group, 56 (80%) cases fell under modified Mason's classification type Ill, totaling 56 instances. The remaining 20% of the cases, amounting to 11 cases, were classified as modified Mason's classification type IV. Among the 70 patients, 55 cases (78.58%) did not have any ligamentous damage, whereas seven (10%) cases had lateral ulnar collateral ligament (LUCL) injury and eight (11%) cases had medial collateral ligament (MCL)injury. The P value for flexion, extension, pronation, and supination was shown to be very significant. Out of the total, 47 (67%) instances had an MEPI score (Mayo Elbow Performance Index) of more than 90, indicating exceptional performance. In addition, 16 cases (22.85%) had an MEPI score ranging from 75 to 89, which is considered a good result. Lastly, seven cases (10%) had an MEPI score ranging from 60 to 74, indicating a fair result. Conclusion The use of a radial head prosthesis is considered a viable option for managing severe and irreparable fractures of the radial head. Effective outcomes hinge on meticulous preoperative planning, skilled intraoperative techniques, and intensive postoperative rehabilitation. These elements collectively contribute to achieving consistent and favorable results in patients undergoing this surgical intervention.

Adrenal gland injury in trauma patients and its impact on clinical outcomes.

Adrenal gland injury (AGI) associated with trauma is an uncommon and often overlooked condition. This study aimed to evaluate the frequency of AGI in individuals with severe trauma injuries and investigate the outcomes of patients with AGI. All patients admitted to a tertiary trauma referral center under the trauma protocol who had a computed tomography (CT) scan between January 2012 and January 2023 were analyzed retrospectively. Patients who were dead on arrival and patients with incomplete data were excluded. They were classified into two main groups, adult and pediatric, and further subcategorized by the presence or absence of radiologically evident AGI. Demographic data, mechanism of injury, injury severity scores (ISS), presence of concurrent abdominal injury, and 30-day mortality rates were compared. A separate analysis was performed for factors affecting mortality rates. A total of 1,253 patients were included: 950 adults and 303 pediatric patients. In the adult group, AGI was detected in 45 (4.7%) patients and was more commonly associated with the following mechanisms of injury: motor vehicle accidents (26.7% vs. 14.3%) and pedestrian accidents (37.8% vs. 15.5%). Injury to the right side was more common (55.6%). Patients with AGI had higher rates of concurrent liver (17.8% vs. 3.9%), spleen (11.1% vs. 3.6%), and kidney injuries (15.6% vs. 1.3%). In the pediatric population, AGI was detected in 30 patients (14.8%), a significantly higher rate compared to the adult group. Similar to the adult group, AGI was more commonly associated with concurrent abdominal injuries and had a right-sided dominance (60%), but the rate of concurrent abdominal injuries was higher in the pediatric group (80% vs. 46%). The 30-day mortality was significantly higher in both adult and pediatric AGI groups compared to patients without AGI (adult: 15.6% vs. 2.9%, pediatric: 10% vs. 1.8%). In patients with AGI, major head and neck injuries and chest injuries were associated with mortality. Adrenal gland injuries due to trauma are not uncommon. They are usually associated with blunt trauma and other concurrent abdominal organ injuries. The major contributors to mortality in patients with AGI were major head and neck injuries and chest injuries.

Alcohol Dependence Among Adult Males in Chengalpattu District, South India: A Mixed Methods Study.

Introduction Post-pandemic alcohol consumption is on the rise due to people starting to adapt themselves to the practice of consuming alcoholic beverages at home. In addition to the direct effects of intoxication and addiction, estimates suggest that alcohol contributes to approximately 20-30% of global cases of oesophagal cancer, liver cancer, cirrhosis of the liver, homicide, epilepsy, and motor vehicle accidents. In India, one-fifth of alcohol consumers were found to be alcohol dependent. The study was done with the primary objective of finding out the prevalence of alcohol dependence among alcohol users and exploring the reasons for alcohol dependence among alcohol users in an urban area of Chengalpattu District, Tamil Nadu. Methodology The study design is an explanatory sequential mixed-methods study. It was done among 624 adult male alcohol consumers in the Chennai district, selected by the cluster sampling method in a community setting. The Alcohol Use Disorders Identification Test (AUDIT) was used to diagnose alcohol dependence. Using the purposive sampling method, in-depth interviews were conducted among 24 alcohol-dependent people to explore and understand their experiences, identify common themes, and provide insights into the problem. Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) version 26 (IBM Corp., Armonk, NY), and qualitative data were analysed using deductive content analysis using Qualcoder software. Results The mean age of the study participants was 38±7 years. Among current alcohol consumers, 16.9% (106/624) were found to be suffering from alcohol dependence. The significant predictors of alcohol dependence were found to be unskilled occupation (adjusted odds ratio [AOR] = 2.09), having suicidal ideation (AOR = 2.4), alcohol consumption by family members (AOR = 1.90), depression (AOR = 3.98), drinking pattern-affected interpersonal relationships (AOR = 2.29), and not receiving health education about alcohol use in school/college (AOR = 1.74). The major themes and codes identified among alcohol dependents were factors related to mental health, physical health, and social factors. Conclusion This study provides essential points of reference for policymakers and primary care physicians to develop prevention strategies for people to understand and overcome the problem of alcohol addiction, and it also sheds light on the burden of alcohol dependence and their lived experiences.

Predictors of Delayed Recovery in Japanese Patients With Whiplash-Associated Disorders: The Role of Initial Catastrophizing and Disability Levels.

Background Whiplash-associated disorders are sequelae of traffic accidents that frequently result in sustained pain and disability due to a broader spectrum of symptoms than typical neck pain. Several studies have used the length of time from injury to the completion of insurance claims as a measure of recovery time for patients with whiplash-associated disorders. However, studies on the initial factors in patients whose treatment exceeds 90 days are lacking. Therefore, this study aimed to identify key factors predicting prolonged treatment duration in Japanese patients with whiplash-associated disorders. Methodology We included 103 outpatients who presented with neck pain after a motor vehicle accident. During their initial visits, various factors were comprehensively assessed, including pain intensity, Neck Disability Index (NDI), six items of the Pain Catastrophizing Scale (PCS-6), a short version of the Tampa Scale of Kinesiophobia, the Injustice Experience Questionnaire, cervical range of motion, and radiographic findings. Patients were categorized into "early recovery" or "delayed recovery" groups based on the time elapsed between the first assessment and the end of the treatment period. Logistic regression analysis identified cut-off values from receiver operating characteristic curves to help identify factors contributing to delays in the recovery process. Results Analysis showed that initial NDI and PCS-6 scores of ≥35% and ≥12, respectively, were significant predictors of delayed recovery, increasing the odds of delay by factors of 3.19 and 4.46, respectively. Conclusions Our findings may aid in appropriate clinical decision-making and lead to interventions to minimize the negative impact of prolonged treatment duration on patient recovery.

Exploring the Effects of Sensorimotor Training and Relaxation Therapy on Postural Control, Balance, Sleep, and Stress in Sedentary Young Adults: Rationale, Design, and Methodology.

Postural control (PC) and sleep are critical in several aspects of health. Poor sleep negatively influences PC and balance, which is necessary for performing various tasks, from reaching to mobility. Moreover, sleep disturbances and consequent PC and balance deterioration are associated with job accidents, traffic accidents, falls, and injuries. Healthy adults who have inadequate sleep show a decline in optimal functioning, even in the absence of medical illnesses. This suggests that getting enough sleep, both in duration and quality, is essential to maintain optimal health. Moreover, inadequate sleep has also been observed to have a bidirectional relationship with stress levels. However, there is insufficient evidence regarding the impact of non-pharmacological treatments to improve PC, sleep, and stress in the sedentary young adult (YA) population. This article describes the protocol for a study to investigate the effects of sensorimotor training and relaxation therapy on various static and dynamic PC tests, balance measures, and subjective and objective indices of sleep and stress among sedentary YAs with impaired sleep quality. The protocol is also designed to evaluate the effect of these therapies on fatigue, salivary cortisol levels, anxiety, and depression. Methods for assessing the sleep architecture, static and dynamic PC, balance, and stress are described along with the methods of scoring with the primary goal of providing a standardized set of assessment and scoring procedures according to the latest guidelines and gold-standard techniques and measures that can be used reliably at different laboratories. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Postural control assessment Basic Protocol 2: Balance assessment Basic Protocol 3: Sleep architecture assessment Basic Protocol 4: Salivary cortisol analysis.

Simulation-Based Enhancement of Patient Safety During Intrahospital Transport of Trauma Patients With COVID-19: A Helipad Scenario.

Trauma resulting from accidents, violence, or war claims over five million lives annually, with traffic accidents and falls being predominant causes. The COVID-19 pandemic has posed unprecedented challenges in trauma care. Even though the number of injuries decreased during the lockdown period, the transportation of trauma patients became even more challenging due to concerns aboutinfection control and the need for enhanced protective measures. This simulation workshop was conducted in a controlled environment to test and refine protocols for the safe transport of trauma patients with COVID-19. Our goal was to develop comprehensive guidance on the intrahospital transportation of these patients, ensuring the highest level of patient care and safety. We detail a five-step approach from preparation to the pre-landing, initial assessment, patient transfer, and patient handover phases, emphasizing adherence to protocols, equipment readiness, and personal protective equipment (PPE) use. The primary issues we encountered were related to time management, the decision-making process for applying lifesaving procedures in an open environment, and the risk of cross-infection. The workshop underscored the importance of swift and coordinated care, balancing life-saving interventions with efficient transport to a definitive care facility.

Towards efficient and accurate prediction of freeway accident severity using two-level fuzzy comprehensive evaluation

Accurately predicting freeway accident severity is crucial for accident prevention, road safety, and emergency rescue services in intelligent freeway systems. However, current research lacks the required precision, hindering the effective implementation of freeway rescue. In this paper, we efficiently address this challenge by categorizing influencing factors into two levels: human and non-human, further subdivided into 6 and 36 categories, respectively. Furthermore, based on the above factors, an efficient and accurate Freeway Accident Severity Prediction (FASP) method is developed by using the two-level fuzzy comprehensive evaluation. The factor and evaluation sets are determined by calculating the fuzzy evaluation matrix of a single factor. The weight matrix is calculated through the entropy method to compute the final evaluation matrix. Based on the maximum membership principle, the severity of the freeway accident is predicted. Finally, based on the experiments conducted with the traffic accident datasets in China and the US, it is shown that FASP is able to accurately predict the severity of freeway traffic accidents with thorough considerations and low computational cost. It is noted that FASP is the first attempt to achieve freeway accident severity prediction using the two-level fuzzy comprehensive evaluation method to the best of our knowledge.

Extracting geo-references from social media text using bi-long short term memory networks

<p>The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches.</p>

When own interest stands against the “greater good” – Decision randomization in ethical dilemmas of autonomous systems that involve their user’s self-interest

Autonomous systems (ASs) decide upon ethical dilemmas and their artificial intelligence as well as situational settings become more and more complex. However, to study common-sense morality concerning ASs abstracted dilemmas on autonomous vehicle (AV) accidents are a common tool. A special case of ethical dilemmas is when the AS’s users are affected. Many people want AVs to adhere to utilitarian programming (e.g., to save the larger group), or egalitarian programming (i.e., to treat every person equally). However, they want their own AV to protect them instead of the “greater good”. That people reject utilitarian programming as an AS’s user while supporting the idea from an impartial perspective has been termed the “social dilemma of AVs”. Meanwhile, preferences for another technical capability, which would implement egalitarian programming, have not been elicited for dilemmas involving self-interest: decision randomization. This paper investigates normative and descriptive preferences for a self-protective, self-sacrificial, or randomized choice by an AS in a dilemma where people are the sole passenger of an AV, and their survival stands against the survival of several others. Results suggest that randomization may mitigate the “social dilemma of AVs” by bridging between a societally accepted programming and the urge of ASs’ users for self-protection.

Functional Outcome of Forearm Fractures Managed With Screw Nails vs. Dynamic Compression Plates: A Prospective Study.

Background Fractures of the forearm are very frequently encountered in day-to-day practice. These fractures have a bimodal age distribution. The forearm fractures are considered intra-articular and need absolute stability for adequate healing. The current treatment modalities include using intramedullary devices such as a square nail, locking intramedullary nail, or using a plate for fixation. In this study, we aim to determine the functional outcome of forearm fractures managed with a screw nail used as an intramedullary device as compared to a locking plate using the Grace-Eversmann criteria. Methodology Patients with forearm fractures were divided into two groups and treated with a screw nail and a dynamic compression plate. Patients were followed up at one month, three months, six months, and one year postoperatively and assessment was done using the Grace-Eversmann criteria. Results The study included a total of 30 subjects, ranging in age from 18 to 65. The majority of the patients had encountered a road traffic accident, following which they incurred a forearm fracture. Grace-Eversmann criteria was used for these patients at follow-up, and a total of 13 patients (86.6%) had good to excellent scores, which was similar when compared to the plate osteosynthesis group (86.6%). A significant difference in the amount of blood loss was noted in the screw nail osteosynthesis group as compared to the plate osteosynthesis group (p<0.05). Conclusions Though a dynamic compression plate is considered a standard method for fixation of the forearm fractures, the use of an intramedullary screw nail as a fixation device gives a similar result with excellent functional outcomes (Grace-Eversmann criteria). It also gives an added benefit of reduced blood loss and preservation of fracture biology.

Ethanol concentrations in various biological specimens: Living and postmortem forensic toxicology analysis and comprehensive literature review

Alcohol use upsurges the risk for many chronic ill-health consequences such as hepatitis, malignancies, and disastrous outcomes like road traffic accidents ending in fatal injuries. Biochemical and toxicological analysis of different body fluids is crucial for identifying the cause of death and postmortem interval in many forensic cases. Blood, urine, and vitreous fluid are the most valuable body fluids for detecting alcohol during any toxicological analysis. Alcohol is responsible for widespread morbidity and mortality worldwide. Blood alcohol concentration (BAC) is a necessary toxicological test to investigate various crime and accident scenes. This study comprehensively explores the demographic characteristics, BAC distribution, and correlations of alcohol concentrations in postmortem and living cases. Postmortem cases (N = 166) reveal intriguing demographic patterns, with notable variations in year distribution, nationality, sex, age groups, occupation, smoking habits, place of death, and psychiatric history. Living cases (N = 483) exhibit distinct demographic profiles, emphasizing differences in year distribution, nationality, sex, age groups, and smoking habits.Analysis of BAC distribution reveals diverse patterns in both postmortem and living cases, providing valuable insights into the prevalence of different BAC levels in each group. Correlation analyses unveil strong associations between alcohol concentrations in various biological samples in postmortem cases, highlighting the interdependence of blood, vitreous, and urine alcohol concentrations. Conversely, living cases display a moderate positive correlation between blood and urine alcohol concentrations.Comparative analyses showcase significant differences in mean alcohol concentrations between postmortem and living cases, suggesting variations in alcohol metabolism and distribution. These findings underscore the importance of considering temporal factors in interpreting alcohol concentrations in forensic and clinical contexts.In conclusion, this study enhances our understanding of alcohol-related incidents by delineating demographic profiles, BAC distributions, and correlations between different biological samples. Such insights are crucial for refining investigative and clinical approaches, contributing to the broader fields of forensic science and public health.

SFT-Net: A Network for Detecting Fatigue From EEG Signals by Combining 4D Feature Flow and Attention Mechanism.

Fatigued driving is a leading cause of traffic accidents, and accurately predicting driver fatigue can significantly reduce their occurrence. However, modern fatigue detection models based on neural networks often face challenges such as poor interpretability and insufficient input feature dimensions. This article proposes a novel Spatial-Frequency-Temporal Network (SFT-Net) method for detecting driver fatigue using electroencephalogram (EEG) data. Our approach integrates EEG signals' spatial, frequency, and temporal information to improve recognition performance. We transform the differential entropy of five frequency bands of EEG signals into a 4D feature tensor to preserve these three types of information. An attention module is then used to recalibrate the spatial and frequency information of each input 4D feature tensor time slice. The output of this module is fed into a depthwise separable convolution (DSC) module, which extracts spatial and frequency features after attention fusion. Finally, long short-term memory (LSTM) is used to extract the temporal dependence of the sequence, and the final features are output through a linear layer. We validate the effectiveness of our model on the SEED-VIG dataset, and experimental results demonstrate that SFT-Net outperforms other popular models for EEG fatigue detection. Interpretability analysis supports the claim that our model has a certain level of interpretability. Our work addresses the challenge of detecting driver fatigue from EEG data and highlights the importance of integrating spatial, frequency, and temporal information.

Analysis of Traffic Accidents on Brigjen Sudiarto Road, Pedurungan District, Semarang City

Analysis of Traffic Accidents on Brigjen Sudiarto Road, Pedurungan District, Semarang City

Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning

Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning

Mild Traumatic Brain Injury by the Glasgow Coma Scale Score and Early CT-Brain Findings in Acute Alcohol Intoxication Patients: A Prospective Observational Study.

Introduction Traumatic brain injuries (TBI) in recent years have proved to be a significant public health problem, with potentially life-changing consequences for the individual and their family. Alcohol consumption is a regular, well-documented problem among persons sustaining TBIdue to road traffic accidents and accidental falls. The primary objective of this study was to find out the correlation between the Glasgow Coma Scale (GCS) score and CT brain findings among mild TBI patients under acute alcohol intoxication and determine if early CT-brain is indicated in this group. Methods A prospective observational study was conducted involving 111 alcohol-intoxicated patients with mild head injuries admitted to the surgical wards of Thanjavur Medical College Hospitalover a period of three months. The Glasgow Coma Scale was used to assess the patient'sneurological status and determine the severity of the brain injury. A semi-structured CT-brain findings chart and a severity of alcohol intoxication objective-based scoring system were developed and validated by experts. Descriptive statistics tools such as frequency, percentage, and mean were used, along with inferential statistics tools like the Chi-squared test, Fisher exact test, and Pearson's correlation coefficient test. Results The study findings showed that the comparison of GCS with early CT-brain was significant at a p-value of 0.012, and a negative correlation (r=-0.253) was found between GCS and CT-brain findings. A comparison of CT-brain findings with the severity of alcohol intoxication was non-significant at a p-value of 0.433. Conclusions Early CT-brain in intoxicated mild TBI patients may have a positive impact on early diagnosis and management, even in centers with limited resources catering to low-income population groups. The results of our short-term study show that early CT-Brain picks up lesions and helps initiate early management while it is up to the attending physician to keep in mind an adverse cost-benefit ratio in overuse of hospital resources and misdiagnosis leading to undertreatment causing long-term sequelae and morbidity before prescribing early CT-brain in this cohort of patients.

A lightweight attention-based multi-frequency topology learning framework for driving fatigue detection

Driving fatigue has been one of the major causes of traffic accident. Efficient and accurate detection of driving fatigue are a legitimate public concern. In this paper, we conduct the simulated driving experiments and an EEG-based driving fatigue detection framework integrating multilayer brain network and convolutional neural network (CNN) is developed. This lightweight attention-based multi-frequency topology learning (AMFTL) framework first captures the fatigue-related multi-frequency brain topological information and then feeds it into a CNN-based topology feature extraction (TFE) module to fully explore and integrate the critical topological features. The quantitative analysis results show that there are significant differences in brain topologies between the alert and fatigue states. And experimental results show that our proposed framework achieves an average detection accuracy of 94.71% for driving fatigue, which outperforms the current state-of-the-art methods. This proposed framework is expected to open new venues for EEG-based brain state analysis, and holds promising practical application potential.

A real-time vehicle safety system by concurrent object detection and head pose estimation via stereo vision

A considerable number of vehicular accidents occur in low-millage zones like school streets, neighborhoods, and parking lots, among others. Therefore, the proposed work aims to provide a novel ADAS system to warn about dangerous scenarios by analyzing the driver's attention and the corresponding distances between the vehicle and the detected object on the road. This approach is made possible by concurrent Head Pose Estimation (HPE) and Object/Pedestrian Detection. Both approaches have shown independently their viable application in the automotive industry to decrease the number of vehicle collisions. The proposed system takes advantage of stereo vision characteristics for HPE by enabling the computation of the Euler Angles with a low average error for classifying the driver's attention on the road using neural networks. For Object Detection, stereo vision is used to detect the distance between the vehicle and the approaching object; this is made with a state-of-the-art algorithm known as YOLO-R and a fast template matching technique known as SoRA that provides lower processing times. The result is an ADAS system designed to ensure adequate braking time, considering the driver's attention on the road and the distances to objects.

Magnitude and determinants of road traffic accidents in North Gondar Zone; Amhara Region, Ethiopia

Background Road traffic accidents are a severe health concern in Ethiopia. This study aims to examine the primary contributing variables of road traffic accidents in Ethiopia, North Gondar Zone, under 24 administrative wereda’s. Methods This study utilized secondary data collected from both the werda-level and North Gondar central-level offices within the study areas. The sector police typically submit reports to the zonal-level offices annually. Information regarding crashes, fatalities, and injuries during the study period was extracted from these police reports. The research focused on analyzing the frequency and severity of traffic accidents attributed to various factors of drivers, vehicles, road conditions, pedestrians, and environmental influences. Results In a sample of 603 aggregate road traffic accidents, the data reveals that the majority of damage results are attributed to young drivers, accounting for 58% of the cases, with junior grade level drivers contributing 29.4%. Driving without a license was a significant factor, representing 87%, while accidents involving recruited driver stood at 63.2%. Vehicles with small to articulated trucks contributing to 39%. Mechanical failures were prevalent, with an unknown diagnosis rate of 48.3%. Vehicles in service for 2-5 years accounted for 29.4%. Road surface conditions were 46%, while rural roads saw a higher percentage at 51.2%. Weather conditions played a role also influential, as dry asphalt and gravel roads under main access ownership were linked to of accident, with 91.2% occurring during visible daylight hours between 8:00-12:00 and 15:00-17:00. The victims were predominantly farmers at 49.6%, followed by students at 23%. Conclusions To reduce road traffic accidents in the study area, it is essential to manage causative factors such as driver and road users, vehicles, roads, pedestrians, and environmental behaviors. The study suggests the development of a successful, up-to-date traffic accident database system regionally within respective offices.