Search Results (406)

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

(1) Background: The number of casualties due to car accidents in Europe is decreasing. However, there are still very large differences in the levels of road safety between countries, even within the European Union. Therefore, it is vital to conduct reliable international analyses to compare the effectiveness of actions taken to prevent road accidents. Information on the number of accidents, injuries, and fatalities can be found in various databases (e.g., Eurostat or OECD). In this paper, it is clearly shown that data on car accidents and the resulting injuries are not comparable between different countries, and any conclusions drawn using these data as their basis will be erroneous. (2) Methods: The indicators of the number of car accidents, injured people, and fatalities in relation to the number of inhabitants were determined, then their distribution and mutual correlations were examined for a group of selected European countries. (3) Results: There is no correlation between the indicators of the number of car accidents and injuries and the indicator of fatalities. An assessment of road safety based on these indicators would result in inconsistent and ambiguous conclusions. (4) Conclusions: It has been empirically shown that data on the number of car accidents and injured people from different countries are not comparable. These conclusions were verified by providing examples of the definitions of an injured person used in different countries. This paper clearly indicates that any international comparisons can only be made based on data regarding the number of road accident fatalities. Full article

To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were reviewed. Among the 164 pedestrian fatalities (involving 92 males and 72 females), the most common scenario involved a pedestrian crossing the road (57.3%). In 61 cases (64.9%), pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side (i.e., crossing from right to left from the driver’s perspective, as vehicles drive on the left in Japan). In 33 cases (35.1%), pedestrians crossed from the vehicle’s lane side to the oncoming traffic lane side. Among cases of pedestrians crossing from the vehicle’s lane side, 54.5% were struck by the near side of the vehicle’s front, whereas 39.7% of those crossing from the oncoming traffic lane side were hit by the far side of the vehicle’s front (p = 0.02). Therefore, for both crossing directions, collisions frequently involved the front left of the vehicle. When pedestrians were struck by the front centre or front right of the vehicle, the collision speeds were higher when pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side rather than crossing from the vehicle’s lane side to the oncoming traffic lane side. A significant difference in collision speed was observed for impacts with the vehicle’s front centre (p = 0.048). The findings suggest that increasing awareness that older pedestrians may cross roads from the oncoming traffic lane side may help drivers anticipate and avoid potential collisions. 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

Road traffic accidents remain a critical global issue with approximately 1.19 million fatalities each year, on which excessive and inappropriate speeds contribute significantly. Managing vehicle speeds is essential for improving road safety, yet predicting and understanding operating speeds remains a challenge. Among different road elements, tangents play a crucial role, as they serve as transition segments between curves and allow for free acceleration, making them particularly relevant for speed management and road design. This study investigates the operating speeds on both single- and dual-carriageway road tangents to identify the key influencing factors. Data were collected from 24 single-carriageway and 20 dual-carriageway road tangents in Croatia, comprising a total of 14,854 speed observations (filtered sample size). The analysis focuses on the impact of geometric, traffic, and roadside environment characteristics on operating vehicle speeds. The results reveal that for single-carriageway road tangents, the most influential factors were traffic volume and terrain type, while for dual-carriageway road tangents, the factors traffic flow density, average summer daily traffic, and heavy goods vehicle share. These findings provide essential insights for the future development of operating speed prediction models, enhancing road design guidelines, and improving speed management strategies. Full article

Background: Hypothermia, occurring when core temperature drops below 35 °C, can lead to death when the body’s heat loss exceeds its heat production. This study investigates two hypothermia-related deaths, exploring the utility of immunohistochemistry, specifically focusing on chromogranin A (CgA) as a potential diagnostic tool. The aim is to assess whether CgA expression in neuroendocrine tissues can be considered a reliable indicator of premortem stress response in fatal hypothermia cases. Case Presentation: In the first case, a 67-year-old man was found on a snowy road 24 h after his disappearance. The autopsy revealed cold-induced skin lesions, gastric hemorrhages, and cerebral and pulmonary edema. Positive CgA immunostaining was observed in the pancreatic islets and adrenal medulla. In the second case, a 49-year-old man was found dead in a wooded area with indications of suicide. Both cases were examined with attention to macroscopic findings and histological samples from major neuroendocrine organs. As in previous cases, CgA immunostaining was positive in the pancreatic islets and adrenal medulla. Staining intensity was moderate to strong, consistent with heightened neuroendocrine activity, supporting the hypothesis of systemic stress prior to death. Conclusions: Although CgA is a potentially valuable adjunct in hypothermia diagnosis, careful consideration of cadaveric preservation is emphasized, particularly when bodies are preserved before autopsy. Further studies with larger sample sizes are needed to confirm its diagnostic specificity and to distinguish true pathological patterns from postmortem artifacts. 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

This paper investigates crash severity in single-vehicle road crashes involving collisions with roadside light poles in Croatia. Due to the absence of detailed object-type classifications in the official crash database, media reports were used to identify relevant incidents in combination with the official state database, resulting in 38 crashes identified between 2016 and March 2025. Descriptive analysis and crosstabulation were applied to explore patterns in crash outcomes. A CHAID decision tree analysis was then applied in an exploratory capacity to highlight possible predictors of injury or fatal outcomes, acknowledging the limitations of the small sample size. Results showed that the speed limit was the only variable significantly associated with crash severity, with all crashes above 50 km/h resulting in injuries or fatalities. The findings highlight the importance of speed management and support the potential for implementing passively safe poles to reduce the consequences of such crashes. The study also discusses the performance of different pole types in line with EN 12767:2019, defines risk zones, and proposes solutions for the example locations. The results offer future research implications and valuable insights for road safety improvement, especially in areas with frequent pole collisions. Full article

Pedestrian behaviour and safety are essential components of urban sustainability. They are influenced by a complex interplay between various factors from different perspectives, particularly in culturally and linguistically diverse (CALD) communities. This study presents a comprehensive overview of the factors influencing pedestrian behaviour and safety with a focus on CALD communities. By synthesizing the existing literature, the study identifies six key groups of influencing factors: social–psychological, cultural, risk perceptions, environmental, technological distractions, and demographic differences. It discovers that well-designed interventions, such as tailored education campaigns and programs, may effectively influence pedestrian behaviour. These interventions emphasize the importance of targeted messaging to address specific risks (e.g., using mobile phones while crossing the road) and engage vulnerable groups, including children, seniors, and CALD communities. The study reveals that CALD communities face higher risks of pedestrian injuries and fatalities due to language barriers, unfamiliarity with local road rules, and different practices and approaches to road safety due to cultural differences. This study underlines the importance of developing and promoting tailored road safety education programs to address the unique challenges faced by CALD communities to help promote safer pedestrian environments for all. Full article

Road traffic accidents pose a substantial global public health burden, resulting in significant fatalities and economic costs. This study employs Bayesian Poisson regression to model traffic accident rates in Poland, focusing on the intricate relationships between weather conditions and socioeconomic factors. Analyzing both yearly county-level and weekly nationwide data from 2020 to 2023, we created four distinct models examining the relationships between accident occurrence and predictors including temperature, humidity, precipitation, population density, passenger car registrations, and road infrastructure. Model evaluation, based on WAIC and PSIS-LOO criteria, demonstrated that integrating both weather and socioeconomic variables enhanced predictive accuracy. Results showed that socioeconomic variables—especially passenger car registrations—were strong predictors of accident rates over longer timeframes and across localized regions. In contrast, weather variables, particularly temperature and humidity, were more influential in explaining short-term fluctuations in nationwide accident counts. These findings provide a statistical foundation for identifying high-risk conditions and guiding targeted interventions. The study supports Poland’s national road safety goals by offering evidence-based strategies to reduce accident-related fatalities and injuries. 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

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

Landslides in Ecuador are one of the most common deadly events in natural hazards, such as the one on 26 March 2023. A large-scale landslide occurred in Alausí, Chimborazo province, causing 65 fatalities and 10 people to disappear, significant infrastructural damage, and the destruction of six neighborhoods. This study presents a detailed case analysis of the anthropogenic factors that could have contributed to the instability of the affected area. Field investigations and a review of historical, geological, and social information are the basis for analyzing the complex interactions between natural and human-induced conditions. Key anthropogenic contributors identified include unplanned urban expansion, ineffective drainage systems, deforestation, road construction without adequate geotechnical support, and changes in land use, particularly agricultural irrigation and wastewater disposal. These factors increased the area’s susceptibility to slope failure, which, combined with intense rainfall and past seismic activity, could have caused the rupture process’s acceleration. The study also emphasizes integrating geological, hydrological, and urban planning assessments to mitigate landslide risks in geologically sensitive regions such as Alausí canton. The findings conclude that human activity could be an acceleration factor in natural processes, and the pressure of urbanization amplifies the consequences. This research underscores the importance of sustainable land management, improved drainage infrastructure, and land-use planning in hazard-prone areas. The lessons learned from Alausí can inform risk reduction strategies across other mountainous and densely populated regions worldwide, like the Andean countries, which have similar social and environmental conditions to Ecuador. Full article

Road transport remains a dominant mode of transportation in Europe, yet it significantly contributes to fatalities and injuries, particularly in crashes involving heavy goods vehicles and trucks. Advanced Driver Assistance Systems (ADAS) are widely recognized as a promising solution for improving truck safety. However, given that the average age of the EU truck fleet is 12 years and ADAS technologies is mandatory for new vehicles from 2024, their full impact on crash reduction may take over a decade to materialize. To address this delay, retrofitting ADAS onto existing truck fleets presents a viable strategy for enhancing road safety more promptly. This study integrates a systematic literature review, cost–benefit analysis, and a pilot field test to assess the feasibility and effectiveness of retrofitting ADAS. The literature review categorizes ADAS technologies based on their crash prevention potential, cost-effectiveness, market availability, and overall efficacy. A cost–benefit analysis applied to the Italian context estimates that ADAS retrofitting could save over 250 lives annually and reduce societal costs by more than €350 million. Moreover, the economic analysis indicates that the installation cost of retrofitted ADAS is outweighed by the societal savings associated with prevented crashes. Finally, pilot field testing suggests high user acceptance, providing a foundation for further large-scale studies. In conclusion, retrofitting ADAS onto existing truck fleets represents an effective and immediate strategy for significantly reducing truck-related crashes in Europe, bridging the gap until newer, ADAS-equipped vehicles dominate the fleet. 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