Accident Location Research Articles

Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future Directions

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

A Passenger Ship Emergency Evacuation Line Efficiency Evaluation Operator Considering Safety Capacity and its Application

Introduction: Aiming at evaluating the efficiency of passenger ship emergency evacuation routes, this study proposes an evaluation operator for passenger ship emergency evacuation routes, and then conducts simulation research on the passenger ship evacuation process. Method: The main innovations of this study are as follows. Firstly, this study proposes an evaluation operator for passenger ship emergency evacuation lines based on consideration of the passage capacity, ship heeling effect, traffic flow obstruction, personnel evacuation mood, accident location, and other factors. Secondly, this study discretizes the emergency evacuation path of passenger ships into a three-dimensional topological network structure and uses simulation data to calculate the traffic capacity between nodes. By comparing the calculated value with the simulated value, a risk assessment method for emergency evacuation is given. Thirdly, this study takes the three-story ro-ro passenger ship in the European "SAFEGUARD" project as an example and gives three passenger ship evacuation simulation processes under different passenger flow densities. Result: The results of the calculation example show that the risk-prone areas are mainly concentrated in the passenger ship stairs, and the risk value increases with the passenger flow. Conclusion: The calculation results verify the effectiveness of the emergency evacuation line efficiency evaluation operator proposed in this study.

Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.

A Novel Framework for Identifying Major Fishing Vessel Accidents and Their Key Influencing Factors

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

Comparative analysis of machine learning techniques for enhanced vehicle tracking and analysis

The past few years have seen a marvellous growth in technology and science. This rapid improvement has proven to be a blessing, making human life easier. Technological developments such as autonomous driving systems and electric cars have made it easier to travel in a dependable and economical manner, satisfying the increasing need for convenient and environmentally friendly travel. However, the increase in traffic has led to a surge in accidents and road casualties. Despite efforts to enhance automobile design and traffic control, there remains a significant need for implementing a system for vehicle tracking, accident detection, and notification. Delays in information and unfulfilled medical needs often result in the loss of lives following accidents. This study reviews and compares different automatic accident detection and notification systems that use accelerometers, vibration detectors, and GPS technology to notify registered contacts of an accident's location via SMS or email. The analysis that follows will specifically look at the benefits, drawbacks, and future uses of various technologies that are used in these systems. In this study, different machine learning-based methods for improving the accuracy of car tracking and cutting down on reaction times in accident situations will be looked at and compared. For testing their usefulness, we used deep learning models like CNN, SVM, and YOLOv3 on a number of different datasets. According to our data, these methods greatly enhance the accuracy of spotting, with YOLOv3 showing the best level of accuracy. Furthermore, the study talks about the pros, cons, and possible future uses of these technologies. It stresses the need for more research into improving model performance in different situations.

An Anthropological Study of People's Attitudes and Beliefs on High Traffic Accident Locations

An Anthropological Study of People's Attitudes and Beliefs on High Traffic Accident Locations

Quantitative risk assessment of coal mine gas explosion based on a Bayesian network and computational fluid dynamics

A gas explosion is one of the most serious coal mine accidents. The complexity and dynamics of gas explosion accidents, resulting from both multiple risk factors and time-dependent physical parameters, necessitates the use of risk assessment techniques to analyze the accident. A quantitative risk assessment method is proposed for coal mine gas explosions based on a Bayesian Network (BN) and Computational Fluid Dynamics (CFD). The results show that, in addition to the problems of “low vigilance” and “substandard safety education”, the “irrational design of mine gas extraction” has become another major risk factor at the new stage of development of mine excavation technology. Three typical gas explosion areas are selected to analyze the possible consequences of an accident. Based on the probabilistic risk assessment model, time-dependent physical parameters such as accident conditions, location and time are analyzed. The probability of occurrence of time-dependent risk factors is quantified to calculate the risk values of the consequences of explosion accidents corresponding to different times. The proposed method helps to improve the calculation accuracy of updating the risk probability value over time and provides theoretical support for the risk analysis of coal mine gas explosion accidents.

Near miss road traffic accidents and associated factors among truck drivers in Gamo zone, southern Ethiopia by using a contributory factors interaction model.

Road traffic accidents (RTAs) are among the leading causes of injuries, fatalities, and the resulting increase in financial burdens worldwide. Every year, RTAs cause numerous serious injuries and fatalities in Ethiopia. it is important to understand how prevalent near-miss crash accidents are, and which by definition could have injured the victim but did not result in an actual accident. The determinants of these near-misses are essential in road crash accident reduction strategies. In spite of the fact that near-miss accidents are much more common than actual losses or injuries, very little research has been conducted on them. Thus, this study was intended to assess the near-miss accidents and associated factors among truckers in Gamo zone, southern Ethiopia. The community-based cross-sectional study was employed from May 12 to July 10,2022, using a structured interviewer-administered questionnaire. A simple random sampling technique was used to select participants. The data were analyzed using the statistical package for social sciences. A binary and multivariate logistic regression model was used to identify the determinants of near-miss accidents. A statistical significance level was set at p < 0.05. About 72.5% of truckers had experienced near-miss road traffic accidents. The majority of the near-miss accidents were caused by speeding, followed by driving on the wrong side of the road and skidding, 65 (22.6%), 39 (13.5%), and 38 (13.2%), respectively. Driving frequency per week, location of accidents, condition of the road, sleeping status, and weather conditions were significantly associated with near-miss accidents. The prevalence of near-miss accidents is high in the Gamo zone. Being a younger and less educated driver, high driving frequency per week, driving on major roads and junctions, foggy weather, and inadequate sleep all contribute to the occurrence of accidents. Road safety measures that could address these identified factors are required to mitigate potential RTAs.

Accident Prevention for Vehicle Safety

Speed is one of the primary wellsprings of vehicle disasters. Many lives could have been saved on the off chance that emergency organizations had gotten the setback information and contacted them on time. This venture manages a mishap recognition framework. In case of a mishap, it involves different parts to call heroes for help. Effective programmed mishap identification with programmed warning of salvage administrations with data about the area of the mishap is a central prerequisite to saving significant living souls. The proposed framework manages mishap cautioning and recognition. Peruse the specific scope and longitude of the mishap vehicle and send this data to the closest crisis administrations. The motivation behind this undertaking is to perceive mishaps and ready salvage administrations on time. As of late the vast majority of the mishap happen because of laziness of drivers in vehicles and trucks. Yearly 1200 passings and 76000 harmed. This methodology incorporates examination of police detailed accident information, top to bottom nearby examinations promptly following an accident of the general driving populace. The present examination was intended to give additional data to traffic wellbeing and others could use in their endeavors to decrease the quantity of sleepy related accidents. than anywhere else in the world, including the more populous China. Calling road fatalities an "epidemic" that will become the world's fifth biggest killer by 2030, the report said while rich nations had been able to lower their death rates, these were sharply on the rise in the third world. It said 90% of deaths on the world's roads occur in low and middle-income countries (21.5 and 19.5 per lakh of population, respectively) though they have just 48% of all registered vehicles. The statistics for India are chilling. At least 13 people die every hour in road accidents in the country, the latest report of the National Crime Records Bureau reveals. However, road safety experts say the real numbers could be higher since many of these accident cases are not even reported. "There is no estimate of how many injured in road accidents die a few hours or days after the accident," points out RohitBaluja, member of the UN Road Safety Collaboration and Commission of Global Road Safety representing Asia. Based on the records, India will become the world number 1 in Road Deaths due to the poor record of average 13 die every hour, which is 1.14 lakh per year. This will make India to be the first place. This causative information about the accidents is the intent to develop the proposed technology as scientific traffic engineering wings to reduce the fatalities due to accidents. This proposed methodology is the automatic system which will provide the solution for identifying the accident location. Nowadays accidents are increasing at an alarming rate. Speed is the cause of most number of traffic accidents. In this project, we first applied Eclat algorithm Keywords— Eclat algorithm, clustering, classification,GPS to group the crime locations into 0 level, 1 level, 2 level accident tracking, Accident.

SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.

Rute Optimal Keterjangkauan Fasilitas Pelayanan Kesehatan Rumah Sakit terhadap Lokasi Rawan Kecelakaan di Kota Padang

This research aims to; 1) map the distribution of accident locations in Padang Timur Subdistrict. 2) analyze health service facilities to accident-prone points. 3) analyze the optimal route of accident-prone locations to public health facilities. This research method uses quantitative methods and the data used are secondary data and primary data. In getting the results of the distribution pattern of the location point, the researcher uses Averagea Nearest Neighbor while to get the results of the hospital coverage using the Service Area method. The results of this study found that the distribution of areas prone to traffic accidents in East Padang Sub-district in 2022 has a clustered pattern. It is known that the Nearest Neighbor Ratio value is 0.75; the z-score value is -2.17; and the p-value is 0.02. The range of hospitals to the location of traffic accidents on the Padang City Bypass highway has a range of 100 m - 2000 m, from six hospitals in East Padang Subdistrict, there are 15 traffic accidents that are reached and 6 traffic accidents that are not reached by the three hospitals.

Smart Accident Detection and Emergency Response System for Enhanced Road Safety

In the realm of road safety, prompt and accurate accident response is paramount to mitigating fatal injuries and saving lives. The existing approach, reliant on manual reporting to authorities or the injured party's ability to communicate with emergency services, introduces delays that can prove fatal. The presented system uses a combination of IoT modules to detect the accident and alert the EMS. It uses an accelerometer to detect the accident, a sim module equipped with a 2G sim to send an alert message consisting of the drivers details, car details, and crash location, acquired by the GPS module to the authorities and few selected individuals. The goal is to drastically reduce emergency response time, improving the likelihood of survival and quick recovery of the injured. This innovative approach not only addresses the current gaps in accident reporting but also contributes to an overall improvement in road safety infrastructure. This work uses a collection of sensors to identify an accident and point out the accident location. This data is sent as a text message to emergency services through which immediate help can be received.

Evaluation of Severity Index of Road Casualties on Mangawan to Chakghat Section of NH-30 Using Machine Learning Approach

Abstract: Each year, over 1.35 million people die in road accidents, and many more are seriously injured, according to the WHO. Predicting the severity of these accidents is vital for improving prevention and response plans. This paper proposes a new method to predict the severity of road accidents using various factors. This research focuses on road accidents at Sohagi Ghat on NH-30. The study looks at accident types and locations along a 3.67 Kilometer section of steep, challenging terrain. A safety audit was conducted on black spots and it revealed that 324 accidents occurred over five years, making this section particularly dangerous. Special safety measures are needed to reduce the risk in this area. The paper aims at forecasting of upcoming highway accidents through moving average approach, exponential smoothing approach and linear forecasting approach using the previous year’s data sets. With the help of Kaggle Data Directory, precise accident data for visualization is formed and then visualization of road accidents is performed on Python programming language-based Anaconda Navigator’s Jupyter Notebook. While accident severity index of Sohagi Ghat NH-30 is also carried out and some results of machine learning based visualization and some forecasting-based results are driven out and compared with each other. The study calculated accident severity and used Logistic Regression to train a model with 0.85 accuracy value. The precision, recall, and F-1 score of the model were perfect. Injury severity percentages were 10.8% fatal, 48.2% serious, and 41% minor. The Accident Severity Index for the NH-30 Sohagi Ghat location was found to be 0.211.

Car Accident Detection and Alert System Leveraging Deep Learning

This paper presents a novel approach to address the critical issue of car accidents through an advanced Accident Detection and Alerting System (ADAS) empowered by cutting-edge technologies in deep learning, computer vision, and communication APIs. With road safety being a paramount concern worldwide, timely detection of car accidents followed by swift alerting mechanisms can significantly reduce the severity of accidents and save lives. The proposed system architecture incorporates the Ultralytics framework, Convolutional Neural Networks (CNN), You Only Look Once version 8 (YOLOv8), OpenCV, and Twilio API. Initially, YOLOv8 is employed to perform real-time detection of vehicles and potential accident scenarios from live video feeds obtained from roadside cameras or vehicle-mounted cameras. Subsequently, CNN models are utilized for comprehensive analysis of detected objects to discern potential accidents based on predetermined criteria, including sudden deceleration, collision patterns, and anomalous vehicle movements. Upon successful detection of an accident, the system triggers an alert mechanism via the Twilio API, which delivers instantaneous notifications to emergency services, nearby medical facilities, and designated contacts of the involved parties. These alerts are enriched with crucial details such as the accident location, vehicle types involved, and severity assessments derived from CNN analyses. Furthermore, the integration of OpenCV facilitates preprocessing tasks such as noise reduction, image enhancement, and object tracking, thereby enhancing the system's robustness and accuracy in diverse environmental conditions and lighting scenarios. The proposed ADAS offers numerous benefits, including real-time accident detection, automated alerting of emergency services, and minimized response times, ultimately contributing to mitigating the severity of accidents and preserving lives. By harnessing the power of deep learning algorithms, advanced computer vision techniques, and seamless communication APIs, this system represents a significant step towards fostering safer road environments within smart cities and transportation networks.

Analysis and Prevention of Road Accidents for Gautam Buddha Nagar District (U.P)

Road accidents pose a significant threat to public safety, necessitating proactive measures for analysis and prevention. This abstract delves into the analysis and prevention of road accidents in the Kasna to Surajpur region over a seven-year period from 2015 to 2021. Through data gathered from multiple police stations, the study aims to identify accident causes, locations, and frequencies to pinpoint blackspots for future road safety audits. Additionally, a novel solution leveraging Pascal's law is proposed to minimize accidents and we can determine the Number of vehicles and Types of vehicles. The system, triggered by vehicle movement, alerts drivers on the opposite side of the road to reduce speed, thus mitigating collision risks. This innovative approach offers promise in enhancing road safety and reducing accidents

Data-driven Bayesian analysis of marine accidents in the English Channel

The English Channel, renowned for its heavy traffic and challenging conditions, frequently witnesses marine accidents. To understand the causal mechanisms and identify risk factors, this study analyzed 833 accidents from 2017 to 2022, employing a Bayesian network predictive model. Validation using log loss, quadratic loss, and spherical gain metrics confirmed the model’s accuracy. The research elucidates the primary influences on accident occurrence: ship type, tonnage, accident location, and flag state. Ship-related factors surpass environmental ones in significance. Older ships face heightened risks of mechanical failures and collisions, especially in adverse weather with limited visibility. Yachts and fishing vessels are particularly susceptible to fire, explosion, collision, and grounding incidents. The study offers theoretical guidance for management departments to devise prevention strategies and enhance navigation safety in the English Channel.

Identifying Spatial Patterns of Road Accidents in Madaba City by Applying Getis-Ord Gi* Spatial Statistic

Road safety has become a subject of great interest among policymakers worldwide as they seek effective strategies to mitigate traffic accidents. There exist various approaches to examine the occurrences of road traffic accidents in terms of their spatial information and consequences. Geographical information systems (GIS) have been widely employed to analyse the spatial patterns of road traffic accidents; they offer various statistical analysis tools to reveal the hotspot locations of road accidents. Reducing the number of traffic accidents and overcoming their negative impact by defining the hotspot locations gain serious attention from the Public Security Directorate (PSD) in Jordan. This study analyses road traffic accidents in Madaba City using spatial statistics to determine the hotspot locations. The Gtis-Ord (Gi*) spatial statistics method was applied to 5730 reported traffic accidents between 2017 and 2019. The results accurately located the groups of chosen accidents and identified 37 hotspots, accounting for 1.89% of the reported cases. The Maximum Z score was 30.99, and 691 reported cases led to the identification of 13 high-priority hotspots. These hotspots occur at significant thoroughfares, busy roundabouts, and uncontrolled intersections. Driving errors and excessive speeding were the most common causes of fatal and non-fatal accidents in Madaba City. Efficient countermeasures to mitigate the number of accidents in Madaba City include adding more police inspectors to the city center, installing speed cameras, and putting up traffic signs at uncontrolled intersections. The outcomes of this work may encourage the PSD to adopt the GIS statistical tools in analyzing the spatial patterns of road traffic accidents to achieve more accurate results.

Variable selection methods for Log-Gaussian Cox processes: A case-study on accident data

In order to prevent and/or forecast road accidents, the statistical modeling of spatial dependence and potential risk factors is a major asset. The main goal of this article is to predict the number of accidents on a certain area by considering georeferenced accident locations crossed with variables characterizing the studied geographical area such as road characteristics as well as sociodemographic and global infrastructure variables. We model the accident point pattern by a spatial log-Gaussian Cox process (LGCP). To reduce the computation burden of LGCP models in this high-dimensional setting, we suggest a two-step procedure: to perform first automatic variable selection methods based on Poisson regression, Poisson aggregation and random forest and in a second step, to use the selected variables and perform LGCP model analysis. The dataset consists in road accidents occurred between 2017 and 2019 in the CAGB (urban community of Besançon), France. Based on LGCP analysis, we are able to identify the principal risk factors of road accidents and risky areas from CAGB region.

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

Real-time risk assessment of multi-parameter induced fall accidents at construction sites

In this research, a risk assessment model was developed that concurrently considers the location and physiological risk factors for fall accidents in workers at construction sites. In terms of location risk, data on 1683 occupational fall accidents involving 1718 worker casualties in the construction industry were collected for the period 1996–2016 from the Institute of Labor, Occupational Safety and Health (ILOSH), Taiwan. Next, the locations of these fall accidents were identified and defined as hazardous areas, with each area assigned a fall-risk level based on the average number of occupational accidents per year. In terms of physiological risk, the thermal hazard evaluation model, Recommended Alert Limits/Recommended Exposure Limits (RALs/RELs), developed by the National Institute for Occupational Safety and Health (NIOSH), USA was used in this study. This model uses metabolic heat and wet bulb globe temperature (WBGT) to recommend hourly rest/work times for workers. Using WBGT and one hour as the basic unit of time, varying degrees of metabolic heat, defined as metabolic heat loading severity (MHLS), were obtained. An optical heart rate (HR) sensor was applied in this study to measure HR, which was subsequently converted to a metabolic heat value using the Malchaire J metabolic heat evaluation model. Next, metabolic heat loading risk level was evaluated based on the MHLS. Finally, the location-based fall-risk level was multiplied by the MHLS, generating a risk evaluation model for fall accidents. The evaluation results identify the real-time fall-risk level of each worker and, if needed, suggest actions to be taken to mitigate the elevated risks of fall accidents identified by the model.