Accident Spot Research Articles

A marine accident analysis based on data-driven Bayesian network considering weather conditions and its application to Taiwanese waters

Few marine accident studies contemplate the weather effects on the places where and dates when shipping casualties occur. This study conducts a marine accident analysis based on Bayesian Network (BN) considering the weather characteristics of the accident spots and occurrence dates. The weather element is adopted as a node in the BN that comprises a group of five clusters illustrating the states of current speed, wave height and wind speed. A total of 431 accidents occurring in the western coast of Taiwan during the period from 2014 to 2022 are analyzed. Wave with height greater than slight sea has a certain effect on the accident frequency and severity. Mishaps are likely to suffer severe consequences if encountering the weather conditions featured by strong current, moderate/fresh breeze and slight/moderate sea. In addition, Accident Type is the most influential node of Accident Severity among the parameters, followed by Ship Type, Gross Tonnage (GT), Weather Cluster, Location and Ship Age. Finally, the three most crucial parameters leading to Very Serious Marine Casualty are Accident Type, Ship Type and GT.

FIRE FIGHTING ROBOT

The increasing frequency and intensity of fire incidents worldwide is maximizing without determined solution hence, there is a growing need of solution for fire extinguishing methods. This system is operated manually with wireless connection, navigates through complex environments to identify and combat fire in diverse setting. Its mobility is enhanced by all terrains such as rough landscapes detecting the obstacle and moving beside to reach the fire accident spot. It is equipped with real time communication capabilities, enabling it to communicate with the coordinator and emergency response team. This advanced system would shape future for efficient and effective firefighting operations by reducing response time, enhancing safety. Keywords – Fire fighting robot, flame sensors, smoke sensor, temperature sensor, motors, water pump, ESP32 camera, ESP32 microcontroller.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Road accidents on Indian National highways, ambulance reachability and transportation of injured to trauma facility: Survey-based introspection of golden hour.

The transportation system plays a crucial role in the context of socioeconomic development, whereas the highway infrastructure acts as a base for the transportation system. In recent years, a rich impetus has been given to the development of road infrastructure by Indian governance. There is a need to introspect how well the prevailing highway infrastructure is equipped with emergency rescue management during road accidents. Lack of ambulance service and trauma facilities along the highways results in a steady loss of lives and injuries and increases people's exposure to risks. This study aims to determine the response time of ambulance reachability to the accident spot on Indian national highways associated with heavy commercial transportation. Also, determining the time to transport the injured to the nearest trauma facility is another factor included as an objective in this investigation. The study adopted survey-based research, whereby the variables in the questionnaire were designed to record and assess the time for an ambulance to reach the accident spot and, from there, to transport the injured to the trauma management facility on Indian highways. Two hundred twenty-five participants who were either victims/relatives of victims or those involved in the rescue of the injured have participated in the survey. The dates of the accident events were 2017 and 2022. The survey resulted in the identification of two categories of highway accidents. The first category of accidents happened on the highways near city limits/dense settlements, and the second category occurred on the core highways. The percentage of accidents caused on the highways either adjacent to or passing through the city limits/dense settlements was reported to be higher than the accidents on the core highways. Ninety percent of the participants reported successful contact with the ambulance call/service centre, but only ~75% success rate exists for ambulances to reach the accident scene. On the core highways, the time taken for the ambulance to arrive at the accident scene is 25-35 minutes. The results from the survey ascertained that the patients were prioritised for treatment in the nearest hospitals (irrespective of having a trauma facility) at a distance of ~12-20 km, for which the time taken is ~15-25 minutes. Importantly, from the interviews, it is understood that in many cases, these hospitals have further referred to specialty hospitals located in nearby cities or trauma centres with greater facilities. Occasions exist where the injured were taken directly to hospitals 30-40 km from the accident spot, for which the time was more than 40 minutes. The results provide evidence that in either of the accident cases on the highways that are adjacent to/passing through the city limits or on the core highways, the total time for emergency care accessibility is nearly 60 minutes or greater; this implies that in the majority of cases, there is very meagre time left to provide emergency medical care to the needy and injured on the Indian highways to abide by the concept of golden hour. Plausible reforms backed by technology for enabling highways into 'emergency rescuable highways' are highly needed to guarantee a safer and more sustainable transportation system in India.

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

Black Spot Location Identification for National Highway: A Case Study of Mugling – Kotre Section of Prithivi Highway of Nepal

The rapid development of road infrastructure enhances connectivity and at the same time increases road crashes. A road traffic accident is one of the leading causes of injury and death in Nepal that has a great impact on the country’s economy. The identification of frequent road traffic crash spots and their causes are essential for the implementation of the most efficient countermeasures. Accident black spots are high-risk spots where numbers of accidents take place frequently and repeatedly. The study focused on the identification of black spots on the Mugling-Kotre section of the Prithivi highway of Nepal to provide knowledge for possible preventive measures for the improvement of road safety. The weighted severity index (WSI), prioritization index (PI), and focus group discussion have been performed to evaluate the safety status in the proposed area. The accident data were analyzed based on the weighted severity index (WSI), and the prioritization index (PI) was estimated by assigning a suitable weight to the factors contributing to an accident. Based on these results, six accident spots have been found critical regarding traffic safety. The result obtained from WSI and PI was further evaluated based on the focus group discussion so that essential safety measures could be implemented to enhance road safety.

Trends in crowd accidents based on an analysis of press reports

Crowd accidents – defined as situations where mass gatherings of people lead to deaths or injuries – have become a frequent occurrence on a global scale. Given the recurring nature of these accidents, it is essential that their characteristics are analyzed. To this end, an important step would be documenting these records. Here, a database of crowd accidents is developed for the period of 1900–2019 through a comprehensive investigation of the press and media reports. The analyses focus mainly on temporal trends of their frequency and injury/casualty in each accident, as well as their geographical distribution and classification based on the purpose of gathering. Results show that the frequency of crowd accidents has been unambiguously on the rise over the last 120 years. Also, there was no indication that larger crowd sizes increase the risk of injury or death per person. In fact, the opposite was the case, although a causal relation between crowd size and risk of injury/death is impossible to establish. Over time, the share of sport events in crowd accidents has declined, and instead, religious gatherings have become more notably present in the statistics. An interesting observation is the association of accident rates to the income level of the countries where they happen, with low-and-middle-income countries being more represented in the records. India and (to a lesser extent) West Africa, in particular, appear to be hot spots for crowd accidents. Finally, it is argued that the exponential increase in crowd accidents of the last century was only partially real, with technology also playing a role in making information more accessible for recent accidents. After the internet (and SNS) became widespread, the trend for reported crowd accidents does not show anymore an exponential increase although it is difficult to conclude whether their frequency is stable or not. The insights obtained from this study can pave the way for developing diagnostic knowledge and raising awareness about the ubiquity of crowd accidents.

Context-Aware Navigation Protocol for Safe Driving in Vehicular Cyber-Physical Systems

A connected network of automated vehicles on roads can increase the driving safety of driverless vehicles (i.e., autonomous vehicles). The critical level of dangerous situations on the road while driving can be increased by the speed, orientation, and traffic density of the vehicles involved. Therefore, there is a need for a maneuvering mechanism that handles both the current driving vehicle and the oncoming vehicles headed toward an emergency zone (e.g., road hazard and road accident spot). In this paper, we present a context-aware navigation protocol (CNP) that enhances the safety of vehicles driving in urban roads. Firstly, CNP includes a collision avoidance module that builds on both vehicular networks and on-board sensors to track vehicles’ behaviors, and this module analyzes the driving risks to determine the necessary maneuvers in dangerous situations. Secondly, CNP establishes a collision mitigation strategy that limits the severity of collision damages in hazardous road during non-maneuverable scenarios. We conducted a theoretical analysis as well as extensive simulations to prove and evaluate the effectiveness of CNP. The results show that CNP can reduce communication overhead from a baseline scheme by up to 60% while the risk of road collisions is less than 5%.

Accident Detection and Ambulance Rescue System using IOT

Abstract: Road accidents and traffic congestion are the major problems in urban areas. Currently there is no technology for accident detection. Also due to the delay in reaching of the ambulance to the accident location and the traffic congestion in between accident location and hospital increases the chances of the death of victim. There is a need of introducing a system to reduce the loss of life due to accidents and the time taken by the ambulance to reach the hospital. To overcome the drawback of existing system we will implement the new system in which there is an automatic detection of accident through sensors provided in the vehicle. A main server unit houses the database of all hospitals in the city. A GPS and Wi-Fi module in the concerned vehicle will send the location of the accident to the main server which will rush an ambulance from a nearest hospital to the accident spot. Along with this there would be control of traffic light signals in the path of the ambulance using RF communication. This will minimize the time of ambulance to reach the hospital. A patient monitoring system in the ambulance will send the vital parameters of the patient to the concerned hospital. This system is fully automated, thus it finds the accident spot and helping to reach the hospital in time. Keywords: IOT, Ambulance Rescue System, Arduino, GPS, Wi-Fi Module

Organ Donation and the Medicolegal Aspects

Context: In India, the majority of brain-dead organ donors are victims of road traffic accidents, thereby making them entities in medicolegal cases (MLC). This places an additional burden and complicates the organ donation process, as an inquest must be conducted by the police station closest to the accident spot before the possibility of organ donation is considered. Following organ retrieval and donation, a postmortem is required before the body is handed back to the police. These procedures cause delays in the organ donation process and subsequently in families receiving the body of their loved ones. The problem is compounded by the fact that there are variations in the inquest and postmortem procedures in different states. Aim: The study aims to comprehensively enumerate the medicolegal protocols followed by the states having an active deceased organ donation program. Design and Methods: A survey, using a structured questionnaire, was conducted among the senior Transplant Coordinators (key stakeholders in the organ donation process) of eight states. Their experiences handling MLC and the state protocols adhered to were captured. Results: It was found that there were differences in protocols followed by the states with respect to medicolegal organ donation cases. Significant variations were observed in the police inquest and postmortem procedures. A need for training of the police personnel in the medicolegal aspects of the organ donation process is evident from the survey responses. Conclusions: The undue delays lead to difficulties in completing the donation and also cause distress among the family members who have said yes to organ donation despite their grief. The study recommends the need for uniformity in protocols to ease pain points in this program. This is of paramount importance in instances of interstate-organ donation cases.

The Proximity of Road Traffic Crash Black Spots to Federal Road Safety Commission Zebra Location/Emergency Health Care Facilities in Federal Capital City, Abuja, Nigeria

This research analyzed the proximity of road traffic crash (RTC) Black Spots to Federal Road Safety Commission (FRSC) zebra locations/emergency health care facilities to ascertain the rescue of RTC victims in Federal Capita City (FCC). Geographic Information System (GIS) based location model was applied in the street network analysis to identify RTC black spots that are outside the close reach of FRSC rescue points/health facilities in FCC. Distance was used as impedance factor. Spatial query and buffer analysis were employed to determine accident spots that fall between 2 kms of the health facility and zebra point. Remote Sensing and GIS techniques were applied in proximity analysis using location strategies and interconnected features. The results were presented on road network maps. The result suggests that five (5) additional zebra points would be effective in terms of proximity to RTC in the area and would considerably improve spatial coverage for response times. The areas where quick response and medical facilities are insufficient were identified. Optimal locations for siting zebra points that can effectively service RTC black spots were proposed.

Application of IoT for Monitoring of Accidents in Agriculture

An IoT based agricultural accidents monitoring system has been developed to know cause and analysis of accident also to estimate losses due to agricultural accidents. It has capability to feed and upload data along with live photographs from the spot of accident and from the place of investigation. The system consists of mobile app and a server-based software. Android Studio and Adobe Dreamweaver was used for development of android app for creating, publishing, and managing websites and mobile content while MySQL, J2EEtechnology were used to develop web applications, CVS is a version control system used for Source Configuration Management (SCM). Eclipse was used to develop packages and Apache Tomcat for web server environment in which Java code can run. The developed multi lingual application is user friendly with multilevel security and can be operated by persons using any android-based mobile phone. The system can generate reports as per the access authentication. The analysis includes accidents type and nature, total number, farm machinery related, hand-tools related and other accidents. Apart from research organizations this system can revolutionize safety in agriculture through manufacturers, policy makers, insurance companies for implementation of more safety features, safety standards on the equipment, creating awareness whereby bringing down accidents to the minimum level for more safe tomorrow in the field of agriculture.

Identifying the (Hot Spots) for traffic accidents in Erbil City A study of the problems of the Urban Environment

Identifying the (Hot Spots) for traffic accidents in Erbil City A study of the problems of the Urban Environment

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi'an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures.

Intelligent Accident-Detection and Ambulance - Rescue System

Accidents are the major causes of death, be it in today's most technologically advanced world. Road accidents and traffic congestion are the major problems in urban areas. Currently there is no technology for detection of accidents. Also due to the delay in reaching of the ambulance to the accident location and the traffic congestion in between accident location and hospital increases the chances of the death of the victim. There is a need of introducing a system to reduce the loss of life due to accidents and the time taken by the ambulance to reach the hospital. Yearly lots of visually impaired people lose their lives by being victims of such accidents. To overcome the drawback of existing system we will implement the new system in which there is an automatic detection of accident through sensors and it will send the location of the accident to the Nearest Hospital, Police station and to the family members which will rush an ambulance from a nearest hospital to the accident spot. This will minimize the time of ambulance to reach the hospital. To achieve this we are using like MEMS (Micro Electro Mechanical Systems) (MPU6050), And using GPS module we will get the location where the accident took place and by using NODEMCU and IFTTT messaging protocol we will send the message of obtained location. Along with this there would be control of traffic light signals in the path of the ambulance using RFID TECHNOLOGY. This system is fully automated, thus it finds the accident spot, controls the traffic lights, helping to reach the hospital in time.

Smart Helmet

According to the investigation in India, nearly 25% of the road accidents are caused by two wheelers. The foremost causes for the fatalities are due to drunken driving, rash driving, and drowsiness due to long drive. The aim is to build an interesting smart helmet that protects us from accidents and indicates the accident prone area. Here we are using various sensors to build the smart helmet. To detect alcohol consumption of the rider we use Alcohol sensors. In order to check a rider’s helmet, an infrared sensor can be used. Vibration detector is also added to the helmet to indicate the harsh hitting of the helmet during an accident. When the two wheelers slide down due to road rashes, the GPS is used to identify the location of the accident spot and quickly sends messages (location) to police stations and hospitals nearby through GSM.

Spatial Cluster Analysis of Fatal Road Accidents From Non-Use of Seat Belts Among Older Drivers

Identifying county-level spatial clusters of fatal accidents due to non-use of seatbelt among drivers 65 years and older can help with injury prevention policies and targeted place-based interventions. We estimated the odds and identified hotspots of fatal accidents among drivers 65 years and older (n=57,715) based on a cross-sectional analysis of data from 2010 to 2018 from the Fatality Analysis Reporting System. The outcome variable was fatality status (fatal/non-fatal), and the main independent variable was seatbelt use (not used/used). Other covariates were drunk driving, distracted driving, and speeding while age, gender, and airbag deployment were used as confounders. Rural-urban status of accident location was used as an effect modifier. Odds ratios were calculated from logistic regression. The age-adjusted fatality rate was computed as the crude fatality rate per 100,000 population weighted by the average population composition by age-groups. Spatial autocorrelation was assessed by local Moran’s I, and cluster analysis was performed using the Moran’s I index-derived Z-scores. The median age-adjusted seatbelt-related fatality rate per county was 2.35 per 100,000 population (IQR: 5.60). Not wearing a seatbelt was associated with an 11-fold (Adjusted OR: 11.37; 95% CI: 10.18-12.70) increased odds of a fatal event in metropolitan counties and a 7-fold (Adjusted OR: 7.43; 95% CI: 6.10-9.04) increased odds in rural counties and small towns. Hot spots for seatbelt-related fatal road accidents were found in multiple counties in Texas, South Dakota, and Mississippi. Study findings can be used for county-specific interventions tailored to 65 and older for preventing fatal road accidents.

Road Accidental Analysis and Identify the Black Spot

The increase in motor vehicles accompanied with expansion of roads has brought with it the challenge of addressing the adverse influence of road traffic accidents. Road incidents are a nationwide cataclysm with ever increasing trend which incurs a human health and development challenge and highly affect the human capital development of every nation. A mortality rate database (WHO, 2002) assures that India has highest number of accidents. Accident Black Spots are those locations on the road which have higher severity of accident either in terms of numbers or in terms of injuries. In this paper, efforts have been made to identify the location of accident black spots on national highway from singhpora pattan to panthachowk srinagar.The present era accidents are contributing major deaths worldwide due to increase in vehicular traffic.It has been estimated that every year 1.38 lakh lose their limbs and 6 lakh people get injured in road accidents in india.our country incurs a loss of 58000 crore each year due to road accidents. The most used roads to travel from jalandhar in punjab to uri in kashmir is NH-1A.The old name for NH-44 is NH-1A which was replaced after renumbering of all national highways in year 2010. In this report,the analysis includes study of some major accident spots or busy intersections in the NH-1A in between Pantha chowkh Srinagar to Singhpora Pattan.The study includes visiting sites,collecting required data for analysis and derives the results and remedies from what we had got. This report will be containing the material surveyed,pictures of road at different locations ,road details,figures,material for understanding of the unknown and finally the conclusion

Development of Model for Road Crashes and Identification of Accident Spots

The aim of this study is to assess the safety of multi-lane rural highway in India. This paper shows the application of a generalized linear modeling technique for the analysis of road accidents on the Indian National Highway. Speed, traffic flow and road characteristics data on four-lane dived rural highway in Dahod are analyzed. The study proposes a novel approach to include average daily traffic (ADT) and average spot speed (AS) in the accident prediction model for a rural highway. The model has been developed for accidents per km as a dependent variable and significant variables such as Junction density, village settlement nearby, ADT, AS as independent variables. The findings from the model offer a better estimate of accidents for a multilane divided rural highway. Statistical Models cannot fully reflect the characteristics of each section due to the heterogeneous nature of road accidents, so the association rule mining technique has been used to identify accident spots as it can deal with the heterogeneous nature of accidents. Accident spots have been assessed by correlating various attributes to the severity of the accident (fatal, non-fatal). This research will help to improve road safety on rural highways.