Search Results (172)

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

In Italy, the law on road homicide (Law no. 41/2016) introduced specific provisions for drivers who cause severe injuries or death to a person due to the violation of the Highway Code. The use of alcohol or drugs while driving constitutes an aggravating circumstance of the offence and provides for a tightening of penalties. Our study aims to report on the analysis performed on blood samples collected between January 2018 and December 2024 from drivers convicted of road homicide and who tested positive for alcohol and/or drugs. The majority of the involved subjects were males belonging to the 18–30 and 41–50 age groups. Alcohol, cocaine and cannabinoids were the most detected substances and the most frequent polydrug combination was alcohol and cocaine. We also investigated other influencing factors in road traffic accidents as the day of the week and the time of the day in which fatal road traffic accident occurred, and the time elapsed between the road accident and the collection of biological samples. Our data, in line with the international scenario, strongly support that, in addition to the tightening of penalties, raising awareness plays a key role in preventing alcohol- and drug-related traffic accidents by increasing risk perception and encouraging safer driving behaviors. Full article

Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment. Full article

Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. To investigate the underlying factors contributing to these accidents, we developed a multifactorial dataset integrating 250,000 accident records from 2015 to 2023 with spatially interpolated weather data and sociodemographic indicators. We employed Kriging interpolation to convert point-based weather station data into continuous surfaces, enabling the attribution of location-specific weather conditions to each accident. Following comprehensive preprocessing and spatial analysis, we generated visualizations—including heatmaps and choropleth maps—that revealed distinct regional patterns at the county level. Our preliminary findings suggest that accident occurrence and severity are driven by different underlying factors: while temperature and vehicle counts strongly correlate with total accident numbers, humidity, precipitation, and road infrastructure quality show stronger associations with fatal outcomes. This integrated dataset provides a robust foundation for Bayesian and time-series modeling, supporting the development of evidence-based road safety strategies. Full article

Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety. Full article

The World Health Organization reports approximately 1.35 million fatalities annually due to road traffic accidents, with pedestrians constituting 23% of these deaths. This highlights the critical need to enhance pedestrian safety, especially given the significant role human error plays in road accidents. Autonomous vehicles present a promising solution to mitigate these fatalities by improving road safety through advanced prediction of pedestrian behavior. With the autonomous vehicle market projected to grow substantially and offer various economic benefits, including reduced driving costs and enhanced safety, understanding and predicting pedestrian actions and intentions is essential for integrating autonomous vehicles into traffic systems effectively. Despite significant advancements, replicating human social understanding in autonomous vehicles remains challenging, particularly in predicting the complex and unpredictable behavior of vulnerable road users like pedestrians. Moreover, the inherent uncertainty in pedestrian behavior adds another layer of complexity, requiring robust methods to quantify and manage this uncertainty effectively. This review provides a structured and in-depth analysis of pedestrian intention prediction techniques, with a unique focus on how uncertainty is modeled and managed. We categorize existing approaches based on prediction duration, feature type, and model architecture, and critically examine benchmark datasets and performance metrics. Furthermore, we explore the implications of uncertainty types—epistemic and aleatoric—and discuss their integration into autonomous vehicle systems. By synthesizing recent developments and highlighting the limitations of current methodologies, this paper aims to advance the understanding of Pedestrian intention Prediction and contribute to safer and more reliable autonomous vehicle deployment. Full article

This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. The number of fatalities on public roadways was then modeled using a multilayer perceptron artificial neural network employing the significant variables as predictors according to the generalization capacity of complex predictive models. The OLS and TOBIT findings indicated that the variables motorcycles and scooters per capita, municipal human development index, and number of SUS emergency units were the most important for modeling traffic fatalities at the scene at the national and regional levels. Applying these variables, the neural network’s best results achieved a hit rate of 88% for Brazil and 95% for the Northeast model. The contribution of this study is providing an approach combining various methods and considering a range of variables influencing traffic fatalities at the scene. The findings offer insights for policymakers, researchers, and practitioners involved in road safety initiatives, mainly where crash data are scarce, and macro-level analysis is necessary. Full article

Background: Road traffic injuries (RTIs) significantly contribute to disability and death in Qatar. This observational study aimed to explore RTI mortality and injury severity trends from 2011 to 2022. Methods: Data from the national trauma database were analyzed retrospectively for mortality rates, injury severity, and characteristics of the injured populations over the years (2011–2022). Results: RTIs represented around 61.3% (n = 12,644) of 20,642 trauma hospitalizations over 12 years. The aggregate RTI mortality rate decreased from 12 to 8 per 100,000 persons, with a mean patient age of 31.8 years. The sum of deaths was 2464, comprising 1022 (41%) in-hospital and 1442 (59%) out-of-hospital fatalities. Among in-hospital deaths, bike-related mortalities totaled 35 (3%), motorcycle-related mortalities 53 (5%), motor vehicle mortalities 561 (55%), and pedestrian mortalities 373 (36%). Based on the injury severity score (ISS), RTIs were divided into four categories, namely, mild (ISS: 1–9), moderate (ISS: 10–15), severe (ISS: 16–24), and fatal (ISS: 25–75). The ISS ranged from 12 to 14, while the median ranged from 10 to 12. The injury frequency showed that mild injuries comprised 40.6% (4545), moderate injuries 26.2% (2934 subjects), and severe 16.7% (1873 subjects). Profound injuries accounted for 13.3% (1490 subjects). Severe and fatal injuries combined dropped from 30% in 2011 to 25% by 2022. Inversely, moderate injuries increased from 24% to 30%, representing a downward trend of the injury severity. Motorcycle-related injuries rose from around 3% to 28% between 2011 and 2022. Motor vehicle and pedestrian injuries declined from about 67% to 54% and 27% to 15%, respectively. Winter, Autumn, Spring, and Summer accounted for 27%, 26%, 24%, and 23% of the total injuries (11,153), respectively. Conclusions: RTI in-hospital mortality and injury severity decreased over the study period. Injury prevention programs should target frequent injury seasons and high-risk populations, such as motorcyclists. Full article

Traffic accidents have become a pressing global public health concern, contributing to millions of deaths and injuries each year. Similar to many countries, the Kingdom of Saudi Arabia is facing significant challenges to overcome the burden of traffic-related injuries and fatalities, prompting the need for effective intervention measures. With the latest advances in sensor fusions, detection, and communication technologies, Automated Traffic Enforcement Systems (ATES) have gained widespread popularity as a solution to improve road safety by ensuring compliance with traffic laws. The objective of this study is to review the effectiveness of ATES in reducing traffic accidents and improving road safety and to identify the challenges and prospects it faced during its implementation. This review uses a detailed overview of different types of ATES deployment, including speed cameras, red-light cameras, and mobile enforcement units, and a comparison between global case studies and local research findings, with special emphasis on the context of Saudi Arabia. This study uses a systematic literature review methodology, using the PRISMA 2020 Protocol, and conducts a scientific literature database search using specific keywords. This study finds that ATES has emerged as an effective tool to ensure traffic compliance and improve overall traffic safety and that various ATES devices have been profoundly effective in reducing traffic crashes. This review concludes that ATES can be an effective solution to improve road safety, but ongoing evaluations and adjustments are necessary to address public perceptions and ensure equitable enforcement. Full article

Every year, more than 700,000 people die by suicide worldwide, a quarter of whom are between 15 and 29 years of age. In Spain, suicide has surpassed road traffic accidents as the leading non-natural cause of death in this age group. Although its overall incidence remains low, the number of suicide attempts continues to rise, indicating an upward trend. Despite being recognized as a significant public health issue, the media often refrains from reporting on suicide to prevent the Werther effect, thereby avoiding the potential propagation of suicidal behavior. This is a form of self-censorship in the exercise of freedom of information, a right recognized by the Spanish Constitution, which also undermines the right of citizens to receive such content. The Spanish Constitutional Court has determined that public interest is a mandatory requirement to endorse the legitimacy of a news item in case of a clash with any of the rights that legally limit freedom of information. This article aims to analyze whether, in those exceptional cases in which the rule of silence is broken, the information on suicide in young people is in line with the jurisprudential concept of public interest, above privacy, honor or self-image and, especially, above the protection of children and adolescents. As a research method, this study analyzes a selection of news articles on suicides of minors, published in Spanish digital newspapers and compiled into a self-developed database. These articles are examined through the lens of the Spanish Constitutional Court’s doctrine on freedom of information. The findings indicate that public interest is unequivocally justified when news coverage focuses on aggregated data regarding suicide or suicide attempts among minors. However, when reporting on the suicide of an individual minor, the justification from the perspective of freedom of information depends on the specifics of each case, requiring a careful balance between public interest and the protection of fundamental rights. Full article

Road safety is a critical concern, as accidents happen globally. Despite efforts to enhance roads and enforce stricter driving rules, the number of accidents remains high. These issues arise from distracted driving, speeding, and driving under the influence. In the United States, fatal accidents increased by 16% from 2018 to 2022. The number of deaths rose from 36,835 in 2018 to 42,795 in 2022. This trend reveals a critical need for new solutions to reduce traffic incidents and improve road safety. Machine learning (ML) can help make roads safer and reduce traffic-related deaths. This paper presents an ML-based real-time highway traffic incident forecasting system named “VehiCast”. VehiCast utilizes vehicular ad hoc networks (VANETs) and federated learning (FL) to collect real-time traffic data, such as average delay, average speed, and the total number of vehicles across several highway zones, to enhance traffic incident prediction accuracy in real-time. Our extensive experimental results showcase that VehiCast reaches an impressive prediction accuracy of 91%, highlighting the power of innovation and determination. Full article

Alcohol is responsible for an ever-increasing number of deaths worldwide, and many road accidents are caused by irresponsible drinking and driving. The use of biomarkers that can support a diagnosis of alcohol abuse is a very important tool that can improve the prevention of many alcohol-related diseases and serious traffic accidents. The main aim of our study was the full validation of a rapid and simple method by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to detect ethyl glucuronide in hair (hEtG). The method was successfully applied to n = 171 real hair samples collected from drivers convicted of driving while impaired by alcohol or drugs. A comparison of hEtG and serum Carbohydrate-Deficient Transferrin percentages (% CDT) was also performed to carefully evaluate the data in relation to the specific detection windows of the two different biomarkers. Most of the drivers with hEtG > 30 pg/mg were males in their thirties. None of the hEtG-positives had a serum % CDT above the cutoff (≥2%). Although some researchers suggest caution until solid data are available on the possible effects of interindividual variability that may influence EtG incorporation and metabolism, hEtG is a very useful biomarker of long-term alcohol exposure that shows greater reliability than traditional blood markers. Full article

Urban air mobility (UAM) and drones can significantly improve traffic movement in saturated cities because the skies above them are not frequently used; furthermore, they do not require large-scale infrastructure, like roads and subways do. Thus, UAM vehicles and drones present themselves as new means of transportation in cities. They can be rapidly deployed if their operational safety is secured. However, to date, no precise numerical study has been conducted on the safety of UAM vehicles and drones. In this study, the accident rates of UAM vehicles and drones are predicted based on the accident rates of conventional aircraft. Additionally, control measures for UAM vehicles and drones are presented at a basic level. The results can be summarized as follows: First, in terms of accident rates, for a projected total UAM vehicle flight distance of 650 km and 177,147 h of flight in Seoul in 2035, 0.000221 crashes, 0.45 takeoff/landing accidents, and 0.0011446 deaths are expected. Second, if drones handle 0.5% of the logistics in Seoul in 2035, 38.35 crashes and 7.51 takeoff/landing accidents are projected per year. However, these numbers are plausible only if the infrastructure required for UAM vehicle and drone flights, such as taxiways and flight paths, is built similarly to that for large aircraft. Additionally, UAM vehicles and drones, as with large aircraft, can cause serious damage to facilities and human lives on the ground in the event of a crash. Therefore, thorough response mechanisms for crashes are required even if the crash probability is extremely low. Finally, integration with smart city systems is suggested to monitor UAM vehicle and drone flights and the safety of urban residents. The transportation services of smart cities include emergency dispatch and disaster notification services, which help in immediately notifying the degree of risk to potentially affected urban residents and facilities in the event of a UAM vehicle/drone crash or an emergency. The transportation services of smart cities are also typically equipped with accident handling processes. Therefore, integrating UAM and drone systems into smart city systems is highly recommended. Full article

Road traffic accidents cause nearly 1.3 million deaths and around 50 million injuries each year globally. Most organisations generate travel and transport, and influence thereby traffic safety. In Sweden, 47 percent of fatal accidents in road traffic are work-related, and 36 percent are linked to a procured transport service. Effective approaches to managing traffic safety in organisations have not yet been established to advance traffic safety implementation, especially through sustainability practices and reporting. This study explores the untapped potential of improving traffic safety by addressing the traffic safety impact of organisations within the context of sustainability. Firstly, this study examines organisations’ views on traffic safety as a sustainability issue, and the status and driving forces in handling traffic safety as an integrated part of sustainability practices and reporting. Secondly, it identifies enablers for advancing traffic safety implementation in organisations with a focus on the sustainability context. The study is based on interviews with 22 organisations (mainly private companies) and analysis of 23 sustainability reports. It is concluded that sustainability is a relevant context for traffic safety for all organisations that consider traffic safety as a significant sustainability issue due to the travel and transport generated, directly or indirectly. However, traffic safety is generally not viewed as a sustainability issue and is rarely included in sustainability reports. Transport companies are more likely to consider traffic safety in the context of sustainability. Enablers concern the necessity to communicate traffic safety as a sustainability issue and to raise awareness of traffic safety as part of the 2030 Agenda for Sustainable Development. Communication is needed to raise awareness among private and public organisations on their traffic safety footprint and possibilities to influence traffic safety. Furthermore, legislative directives and standards on sustainability reporting should explicitly include traffic safety. By integrating traffic safety in sustainability practices and reporting, organisations can more clearly draw from and utilise the positive synergies between traffic safety and other sustainability goals. Furthermore, such integration is a way to bring traffic safety issues up to the management level, facilitating leadership for traffic safety. Full article

Pedestrian safety is a critical concern worldwide, as pedestrians account for nearly a quarter of all road crash deaths. In Poland, in the last decade, the number of pedestrians killed in road accidents varied from 25 to 30% of all road accident victims each year. A similar tendency is observed in EU countries, but the average number of pedestrian fatalities is lower and amounts to 20%. Numerous activities have been undertaken to improve the safety of vulnerable road users. Land planning plays a crucial role in enhancing pedestrian safety. Effective land-use planning can mitigate risks by integrating pedestrian-friendly infrastructure into urban design. Numerous measures have been implemented to improve the safety of vulnerable road users, including education campaigns, speed reduction measures, and infrastructure enhancements. One of the latest initiatives involves enhancing the visibility of pedestrian crossings through the installation of additional lighting systems. In order to assess the effects of the undertaken activities, a number of zebra crossings with and without additional luminance were investigated. Crash data gained from police statistics, along with the calculated crash rates (CRs), were utilized to evaluate changes in safety performance at selected crosswalks. For this purpose, a „before–after” method was applied. Importantly, the research results did not show a clear impact of additional lighting on reducing the number of road crashes and they highlight that other factors, including the geometric characteristics of crossings and their location and proximity to land uses generating significant pedestrian traffic, significantly influence crash rates. Full article