Fatal Road Accidents Research Articles

Fatal road traffic accidents and injuries: a preliminary study.

Road traffic accidents (RTAs) are a major public health problem globally and cause deaths, disability, and significant economic costs. In Italy, there was an increased number of road accidents, deaths, and injuries in 2022 compared to 2021, although still below pre-pandemic levels. A retrospective observational study was performed on a case series of 53 consecutive fatal RTAs examined by the Section of Forensic Medicine of Verona. The case series was divided, according to the type of victims involved, into pedestrians, car drivers, car passengers, and motorcyclists. For each, the times and causes of death, the distribution of external and internal injuries, and toxicological data were analysed, and the results were compared with those in the literature. Although this is a preliminary study on a small case series and lacks statistical validation, so more cases are needed, the preliminary results seem to provide a useful tool for assessing injuries in complex fatal road accidents.

Pattern and distribution of injuries associated with road traffic accident in Haldwani (Nainital) - An autopsy based study

Indiais one of the fastest growing economies. There is a rapid inflation of vehicles on the road that range from motorcycles to heavy vehicles which also shows an active surge in the fatal road accidents. This study is focussed to find out the pattern and distribution of injuries in fatal road traffic accident cases in Kumaon region of Uttarakhand as to plan at the time of policy making and traffic law implementation.: The present study was conducted in the department of Forensic Medicine & Toxicology of Government Medical College, Haldwani from January 1 2019 to December 31 2019, on total 110 cases of RTA brought to mortuary for medico-legal post mortem examination.: Maximum number of cases sustained head and neck injuries (42 out of 110 externally) followed by extremities injury (28 out of 110). Total 38 cases out of 110 sustained head and neck injuries internally, followed by abdominal injuries (28 cases). Abrasions (30 cases) and laceration (29 cases) were commonest type of external injury noted in RTA cases followed by contusion (22 cases) and fracture (19 cases). Most common cause of death among RTA victims was head injury (40 out of 110 cases) followed by shock and haemorrhage (37 out of 110 cases).

A COMPARATIVE ANALYSIS OF INJURIES AND DEATHS CAUSED BY ROAD TRAFFIC ACCIDENTS IN POLAND AND SELECTED EU COUNTRIES

The article examines the relationship between three dependent variables: the quantity of accidents on roads in Poland and the quantities of those hurt and killed as a result. Data were obtained from police websites between 2016 and 2021. Multidimensional comparative analyses were used for the research, which, by grouping data and compiling them dynamically in various scales depending on the needs, allowed us to observe trends such as seasonality on a monthly basis (data in a month - Fig. 2) and a downward trend (data in years Fig. 3). This became a premise for building a multiple regression model, which allowed us to dynamically observe the correlation between the quantity of accidents in Poland and the quantity of people hurt and killed as a result. The above-mentioned regularity was described by the function: Y = ‒67,9212 + 1,3341*v(2) + 0,7332*v(3) + +28,4305*v(4) and the built model was analyzed and evaluated. Then, information on the quantity of road accidents and quantities of people killed and hurt in the capital city of Warsaw from January 2021 to December 2022 were used for the research and compared with the data observed throughout Poland. To observe the impact of the random factor, which was a strong increase in the scale of fines in 2022, especially for exceeding the limit of speed by approximately more than 31 km/h. Dynamic indices on a constant base were calculated for the research. It was found that in 2022, compared to 2021, there was a visible decline in the quantity of people killed in road accidents in Warsaw from 42 to 30 people. A similar regularity can be observed in the data on fatalities in road accidents throughout Poland in the identical time period [11]. Moreover, there was no correlation between the time series of fatalities in road accidents in Warsaw and the quantity of accidents and injuries in 2022.

Fatal crashes and rare events logistic regression: an exploratory empirical study.

Fatal road accidents are statistically rare, posing challenges for accurate estimation through the classic logit model (LM). This study seeks to validate the efficacy of a rare events logistic model (RELM) in enhancing the precision of fatal crash estimations. Both LM and RELM were employed to examine the relationship between pertinent risk factors and the incidence of fatal crashes. Crash-injury datasets sourced from Hillsborough County, Florida served as the empirical basis for evaluating the performance metrics of both LM and RELM. The analysis revealed that RELM yielded more accurate predictions of fatal crashes compared to LM. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) for each model was computed to offer a comparative performance assessment. The empirical evidence notably favored RELM over LM as substantiated by superior AUC values. The study offers empirical validation that RELM is demonstrably more proficient in predicting fatal crashes than the LM, thereby recommending its application for nuanced traffic safety analytics.

Drivers' performance assessment approaching pedestrian crossings through the analysis of the speed and perceptive data recorded during on-field tests

Pedestrian fatalities in road accidents represent one of the biggest causes of death in the world despite the great efforts that have been made to decrease the involvement of vulnerable road users in road accidents. Literature analysis revealed the presence of several studies aimed at investigating the phenomenon and proposing strategies to improve pedestrian safety, but this is still not enough to considerably reduce the number of pedestrians killed on the road.In this context, with the aim to take a step forward in the topic, this paper describes a naturalistic driving assessment carried out in Firenze aimed at evaluating the effect of different pedestrian crossing configurations on the drivers' behavior, especially concerning the reduction of the speeding phenomenon approaching a pedestrian crossing. The experiment was conducted on a section of an urban collector road within the Firenze suburban area. Crucially, over the past few years, different traffic calming interventions have been implemented along this street. Among the different traffic calming countermeasures, both the presence of a traffic light and trapezoidal deflection have been considered to assess their effect on drivers' behavior, also with reference to specific aspects related to the drivers’ perception. During the experiment, thirty-six users drove their own vehicles along the street, encountering different pedestrian crossing configurations. During the driving speed, deceleration and ocular fixation were recorded.This study shows the difference in drivers' behavior in response to different traffic calming countermeasures. It demonstrates also that the raised pedestrian crossing caused a significant effect on reducing the speed approaching a pedestrian crossing. Moreover, it is observed that, when perceptive countermeasures are present, the drivers’ behavior changes only if the pedestrian crossing configuration is perceived in foveal vision; suggesting that the correct identification of the configuration is crucial to implement a congruent and safe driving behavior.

Comprehensive Analysis of Fatal Road Accidents: Patterns and Characteristics of Injuries in a Forensic Medicine Setting

An accident is an occurrence that takes place suddenly, unexpectedly and unintentionally, often arising from unforeseen circumstances. Road traffic injuries (RTA/RTI) resulting in death, especially Motor Vehicle Traffic Accidents (MVTA), have been recognized as a concealed epidemic with global impact, affecting every segment of society. This study has been conducted at a tertiary care hospital, aimed to identify different patterns and distributions of injuries, with the goal of devising preventive measures for the future.

Characteristics of motorization's impact on the urban population

It is known that the most important thing in maintaining the good health of citizens is to save their lives from possible fatal road accidents, ensure the cleanliness of the air space, isolation from excessive noise pollution, electromagnetic fields. The listed negative impacts generate intensive traffic flows, which are the main component of transport systems. The task of identifying the patterns of changes in motorization over the past five years has become important. Motoriization, as it was previously believed, is one of the characteristics of the well-being of the population. Growth is already accompanied in large cities by negative phenomena, such as: road accidents with loss of life; deterioration of air pool quality; excessive traffic noise; excessive electromagnetic fields that cause intense traffic flows. All these negative characteristics of the increase in the level of motorization formed the subject of this study. First of all, on the basis of statistical data, a chronological pattern of growth in the indicator of motorization has been found by year were revealed based of statistical data (they are described by showing filamentous and rectilinear equations): there is a constant increase for the period from 2017 to 2022. Functional dependencies of the influence of motorization levels on each of the hazards were also found: the influence of motorization on fatal road accidents and air pollution is described by exponential equations, and noise and electromagnetic ones are described by straight-line non-decreasing equations. Based on the obtained results, conclusions were drawn regarding the reduction of their impact by replacing conventional transport systems with intelligent transport systems with elements of artificial intelligence.

Qualitative/Quantitative Analysis of Alcohol and Licit/Illicit Drugs on Post-Mortem Biological Samples from Road Traffic Deaths

Alcohol and drug abuse is a major contributory factor of all road deaths in Europe. The aim of this study is to investigate the prevalence of alcohol and licit/illicit drug intake among victims of road accidents in Campania region (Italy). A retrospective analysis of road traffic deaths from 2013 to 2022 in Campania was performed. The toxicological results from fluid samples collected at autopsy were reviewed. In total, 228 road deaths occurred, mostly during nights and weekends. A total of 106 victims tested positive for alcohol and/or drugs, among which 39 (36.8%) tested positive for alcohol only, 27 (25.5%) for alcohol and drugs in association; and 40 (37.7%) for licit/illicit drugs only, either individually or in combination. Polydrug intake has been found in 21 victims, and nine in combination with alcohol. The most detected drugs were cocaine and Δ9THC, followed by benzodiazepines. Blood alcohol concentration (BAC) > 1.5 g/L was found in most alcohol positives, both alone and in association with drugs. Despite the penalties for driving under the influence of alcohol (DUI) and drugs (DUID), no decrease in the number of alcohol and/or drugs related fatal road accidents has been observed. DUI and/or DUID cases were approximately one third of the entire sample study.

Analysis of road accident mortality based on time of occurrence for Kerala, India

Background: Road traffic accidents (RTA) pose a significant socio-economic burden and global public health concern. Monitoring road safety initiatives' efficacy necessitates analysing RTA incidence. This study examines time zone-specific RTA mortality in Kerala state, India, from 2016 to 2021. Methods: Utilizing compiled secondary-level time series data, the study encompasses total RTA fatalities in Kerala from 2016 to 2021. Data includes fatalities per year in nine consecutive three-hour time periods. Exploratory data analysis, time series regression, and exponential smoothing were employed for analysis. Results: Data reveals fluctuating trends in road accident (RA) fatalities, peaking in 2018 with a notable decrease in 2020. 18:00 to 21:00 recorded the highest and lowest fatalities, total 901 deaths. Disproportionate RA fatalities occurred from 06:00 to 09:00 (527 deaths) and 15:00 to 18:00 (697.5 deaths). The study employs Holt-Winters exponential smoothing for short-term forecasting, with a mean absolute scaled error (MASE) less than 1 signifying accurate predictions. Conclusions: The analysis highlights temporal patterns, emphasizing 18:00 to 21:00 as critical. Holt-Winters exponential smoothing proves vital for accurate short-term forecasting, with MASE reflecting precision. Urgency is stressed in adopting targeted measures for time-specific road accidents. Government intervention is pivotal, advocating for improved infrastructure, enhanced driver education, efficient vehicle management, and sustained traffic enforcement. Tailoring traffic laws to time zones, coupled with forecasting techniques, aligns with the overarching goal of enhancing road safety and reducing RA mortality rates.

Data Mining technique: Application of Apriori algorithm for road accident analysis

Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.

The heart and the sea: on the lifeblood and elemental folds of Réparer les vivants

ABSTRACT In Katell Quillévéré’s Réparer les vivants (2016), based on Maylis de Kerangal’s best-selling novel, a fatal road accident after a surfing trip leaves Simon Limbres (Gabin Verdet) braindead. His vital organs will be donated, and his heart transplanted into Claire Méjean (Anne Dorval). These grave events shape the film’s narrative arc, with the heart connecting Simon’s death to Claire’s rebirth. Yet this heart transplant film is indebted to a more archaic, elemental connectivity between the heart and the sea, blood and seawater, recognised variously from Galen to René Quinton and beyond. A fusion of the elements of air and water with breath positions the film distinctively in relation to scholarship on the elements and on breathing in film and media studies, inviting its watery bonds to be theorised anew. Engaging with such research before drawing upon Gilles Deleuze’s writings on the fold and film scholarship indebted to his work, this article explores how the film’s narrative is enfolded into tight formal bonds with the elements, air and water in particular. The lifeblood of this film and its affective heart emerge through myriad folds of foaming, curving waves.

Identifying the most informative variables to discriminate between fatal and non-fatal road accidents

Road accidents account for millions of fatal and non-fatal victims worldwide each year, causing losses for victims and their families in many ways. Understanding the causes that differentiate fatal from non-fatal accidents is an essential topic in the area of traffic safety. In this way, this paper proposes a framework to identify the most informative variables for discriminating road accidents as fatal and non-fatal. The proposed framework is comprised of three operational steps: (i) Preprocess the data describing road accidents; (ii) Iteratively classify accident events into fatal or non-fatal and eliminate the less relevant variables using the omit-one-variable-at-a-time (O1AT) technique; and (iii) Qualitatively assess the selected variables. For that matter, data reporting accidents in rural and urban areas of Brazil (BR) and Great Britain (GB) in 2018 were analyzed. The proposed framework substantially increased the accuracy of event classification into fatal and non-fatal categories, and reduced the number of variables required for analysis. Frontal collision and pedestrian run over were deemed the most informative variables to describe fatal accidents in Brazil. As for events in Great Britain, variables associated to the period of the day the accident took place, number of vehicles involved, and whether the police attended the accident scene were deemed statistically significant in distinguishing fatal from non-fatal accidents.

Analysis of road accident causes in the voivodships in the years 2014-2021 in the aspect of socio-economic costs – towards the implementation of sustainable development

The article addresses the topic of road safety. The reduction of road accident injuries and fatalities fits in with Agenda 2030 sustainable development goals. The objective of the research was to assess road accidents caused by voivodships. The analysis took account of the socio-economic differences in the voivodships, which determine the unit costs of fatalities and injuries. A descriptive analysis is given to show the dynamics of changes in the years 2014-2021 in the voivodships and how different factors influenced change. The article analyses the relation between road safety and the region’s socio-economic development. Data availability determined the time series applied. As we know from research, accidents fall in relation to the (demographic) density of improved roads. Accidents increase, however, in relation to the density of population and GDP per capita. These relations take the opposite direction if the (demographic) fatality rate or accident severity rate (fatalities per 100 accidents) is the dependent variable. Socio-economic losses were estimated using Statistics Poland data. They are the indirect causes of road accidents which occur as a result of lost production due to premature death or incapacity. To value the unit costs of road accidents, the PANDORA method was used. The analysis helped to group the voivodships for their highest and lowest socio-economic costs of road accidents with a special focus on the unit costs of road deaths. The originality of the research is related to the updated voivodship level research.

The use of the R2 coefficient of determination to determine the impact of selected parameters on the number of fatalities in road accidents in Poland compared to selected European countries in 2010-2020

The article presents statistical data on the state of road transport safety in Poland against the background of selected European countries in the years 2010-2020. The article presents the characteristics of the change in the length of motorways along with the percentage rate of increase in the length of motorways in selected European countries. Statistical data on the number of registered motor vehicles in Poland against the background of selected European countries in the analyzed period are presented. The main purpose of the article is to use the coefficient of determination R2 to determine the impact of the number of registered motor vehicles and the length of motorways on the number of fatalities in road accidents in selected European countries. The authors chose the coefficient of determination R2 as an indicator of how reducing the overall number of road accidents and increasing the length of motorways will affect the number of fatalities. The coefficient of determination is used to determine what percentage of one variable affects the variability of the other variable. The determined high value of the determination index R2 (close to 1) indicates a large impact of the analyzed variable on the number of fatalities in road accidents. The high result of the determination coefficient R2 for Poland in terms of the analyzed variables confirms that a further reduction in the number of fatalities in road accidents is possible thanks to the expansion of motorways and the reduction of the number of motor vehicles.

Improved Thermal and Chemical Stability of Oxynitride Phosphor from Facile Chemical Synthesis for Vehicle Cornering Lights

AbstractOrange Eu2+‐doped phosphors are essential for light‐emitting diodes for cornering lights to prevent fatal road accidents at night, but such phosphors require features of high thermal, chemical stability and facile synthesis. This study reports a series of yellow‐orange‐red emitting SrAl2Si3ON6:Eu2+ oxynitride phosphors, derived from the SrAlSi4N7 nitride iso‐structure by replacing Si4+−N3− with Al3+−O2−. The introduction of a certain amount of oxygen enabled the facile synthesis under atmospheric pressure using the air‐stable raw materials SrCO3, Eu2O3, AlN and Si3N4. SrAl2Si3ON6 has a smaller band gap and lower structure rigidity than SrAlSi4N7 (5.19 eV vs 5.50 eV, Debye temperature 719 K vs 760 K), but exhibits higher thermal stability with 100 % of room temperature intensity remaining at 150 °C compared to 85 % for SrAlSi4N7. Electron paramagnetic resonance, thermoluminescence and density functional theory revealed that the oxygen vacancy electron traps compensated the thermal loss. Additionally, no decrease in emission intensity was found after either being heated at 500 °C for 2 hours or being immersed in water for 20 days, implying both of the thermal and chemical stability of SrAl2Si3ON6:Eu2+ phosphors. The strategy of oxynitride‐introduction from nitride promotes the development of low‐cost thermally and chemically stable luminescent materials.

Improved Thermal and Chemical Stability of Oxynitride Phosphor from Facile Chemical Synthesis for Vehicle Cornering Lights.

Orange Eu2+ -doped phosphors are essential for light-emitting diodes for cornering lights to prevent fatal road accidents at night, but such phosphors require features of high thermal, chemical stability and facile synthesis. This study reports a series of yellow-orange-red emitting SrAl2 Si3 ON6 :Eu2+ oxynitride phosphors, derived from the SrAlSi4 N7 nitride iso-structure by replacing Si4+ -N3- with Al3+ -O2- . The introduction of a certain amount of oxygen enabled the facile synthesis under atmospheric pressure using the air-stable raw materials SrCO3 , Eu2 O3 , AlN and Si3 N4 . SrAl2 Si3 ON6 has a smaller band gap and lower structure rigidity than SrAlSi4 N7 (5.19 eV vs 5.50 eV, Debye temperature 719 K vs 760 K), but exhibits higher thermal stability with 100 % of room temperature intensity remaining at 150 °C compared to 85 % for SrAlSi4 N7 . Electron paramagnetic resonance, thermoluminescence and density functional theory revealed that the oxygen vacancy electron traps compensated the thermal loss. Additionally, no decrease in emission intensity was found after either being heated at 500 °C for 2 hours or being immersed in water for 20 days, implying both of the thermal and chemical stability of SrAl2 Si3 ON6 :Eu2+ phosphors. The strategy of oxynitride-introduction from nitride promotes the development of low-cost thermally and chemically stable luminescent materials.

Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

Road obstacles that unexpectedly appear such as vehicle breakdowns and accidents, are major causes of fatal road accidents. Connected Autonomous Vehicles (CAVs) can be used to avoid collisions to ensure road safety through cooperative sensing and driving. However, the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works. In this paper, we first design a platoon-based collision avoidance framework for CAVs. In this framework, we deploy a Digital Twin (DT) system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme. In addition, an DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system. In this case, the transmission frequency of kinetic states of platoon members to ensure low-overhead communication. Moreover, we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability. To further improve road safety, an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks. Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance. Compared to the existing schemes, it can reduce collisions by 95% and is faster by about 10% passing by the unexpected obstacle.

Real-Time Drowsiness Identification Based On Eye State Analysis

As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD). Key Word: Eye blink detection; Drowsiness Detection; Eye Aspect Ratio (EAR)

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection.

Smart Accident Detection, Prevention and Reporting using Arduino

Numerous people lose their lives in traffic accidents every day. In India, 4,80,652 traffic accidents resulted in a total of 1,51,113 fatalities in 2019. In terms of road accident deaths, India currently retains the top rank. Excessive speed was the primary cause of fatalities in road accidents. To save the lives of several injured accident victims, this is a serious problem that needs to be remedied. In order to address the ongoing challenges surrounding automobile accidents, various automobile companies have implemented a range of safety systems, including safety airbags, seat belts, camera sensors, and more. Despite these efforts, the root causes of accidents and their resulting effects have proven difficult to mitigate entirely. To tackle this persistent issue effectively, there is an urgent need for a comprehensive system that can automatically detect accidents as they occur. This system would play a crucial role in promptly communicating vital information about the accident and its precise location to both nearby hospitals and concerned relatives without any delays. When a car has an accident, the accelerometer immediately provides data to the Arduino, which then sends the alarm message through the GSM MODULE, including the location that is identified by the GPS MODULE, to recently saved crisis contacts.