Search Results (114)

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

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

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

Epidemiological analysis has revealed key insights into the frequency, severity, and circumstances surrounding subway-to-pedestrian incidents; however, there remains a lack of available impact test data specific to this impact type that can be used in modelling and countermeasure design studies. To address this gap, nine controlled impact tests were conducted using a cylindrical headform to derive force–penetration relationships for foam, as well as foam encased in 1 mm aluminium or 3 mm ABS shells. These relationships were validated in MADYMO multibody simulations. Building on a previous multibody computational study of subway-to-pedestrian collisions this research evaluates three passive countermeasure designs using a reduced simulation test matrix: three impact velocities (8, 10, and 12 m/s) and a trough depth of 0.75 m. In subway collisions, due to the essential rigidity of a subway front relative to a pedestrian, it is the pedestrian stiffness characteristics that primarily dictate the contact dynamics, as opposed to a combined effective stiffness. However, the introduction of energy-absorbing countermeasures alters this interaction. Results indicate that modular energy-absorbing panels attached to the train front significantly reduced the Head Injury Criterion (HIC) (by 90%) in the primary impact and pedestrian-to-wheel contact risk (by 58%), with greater effectiveness when a larger frontal area was covered. However, reducing primary impact severity alone did not substantially lower total fatal injury risk. A rail-guard design, used in combination with frontal panels, reduced secondary impact severity and led to the largest overall reduction in fatal injuries. This improvement came with an expected increase in hospitalisation-level outcomes, such as limb trauma, reflecting a shift from fatal to survivable injuries. These findings demonstrate that meaningful reductions in fatalities are achievable, even with just 0.5 m of available space on the train front. While further development is needed, this study supports the conclusion that subway-to-pedestrian fatalities are preventable. 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

The rapid development of electric vehicles (EVs) around the world has resulted in new challenges for road safety. Identifying the features of EV crashes is a precondition for developing effective countermeasures. However, due to the short history of EV development, existing studies on EV crashes are quite limited. China, which has the largest EV market in the world, has witnessed a substantial increase in EV crashes in recent years. Therefore, this study comprehensively investigated the characteristics of EV crashes by analyzing the 2023 traffic crash data from Changshu. This is a pioneering study that discusses EV safety by comparing real EV crashes and ICEV crashes from a city in China, the largest EV market in the world. It was found that EV crashes had a higher fatality rate compared to internal combustion engine vehicle (ICEV) crashes. Compared to ICEV crashes, EV crashes are more likely to hit pedestrians and occur during the starting phase. Among the vehicles involved in crashes, the proportion of EVs used for passenger and freight transport was higher than that of ICEVs. In addition, for EV crashes, the proportion of female drivers was much higher, but the proportion of elderly drivers was much lower. Thus, to identify the significant factors influencing crash severity, a logistic regression model was built. The results confirm that EV crashes are more likely to be more fatal than ICEV crashes. In addition, hitting pedestrians and light trucks and crashes occurring in rural areas, at intersections, during winter, and on weekdays could significantly increase the risk of fatalities. These findings are expected to provide new perspectives for improving EV safety within the wave of automotive electrification. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

This study investigates the various factors contributing to the severity of traffic collisions, with specific attention given to elements such as the involvement of pedestrians and cyclists, the roles played by motor vehicles, prevailing weather conditions, road characteristics, and geographical contexts. Drawing from a comprehensive dataset from the Virginia Department of Transportation, encompassing over 500,000 data points, this study utilizes two statistical models. Specifically, it utilizes Multinomial Logistic Regression and Multi-Level (Mixed Effect) Multinomial Logistic Regression, which accounts for group-level heterogeneity, to explore the intricate interplay between various factors and collision severity outcomes. The findings underscore the superiority of the Multi-Level Multinomial Logistic Regression model over the standard Multinomial Logistic Regression model in capturing road user severity. Furthermore, this paper highlights the heightened odds of fatalities associated with the presence of vulnerable road users, such as pedestrians and cyclists. Collisions involving unbelted drivers exhibited odds ratios exceeding 10, indicating a substantially elevated likelihood of severe outcomes compared to their belted counterparts. Full article

Roadway mortality increased during COVID-19, reversing a multi-decade downward trend. The Fatality Analysis Reporting System (FARS) was used to examine contributing factors pre-COVID-19 and in the COVID-19 era using the five pillars of the Safe System framework: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two study time periods were matched to control for seasonality differences pre-COVID-19 (n = 1725, 1 April 2018–31 December 2019) and in the COVID-19 era (n = 2010, 1 April 2020–31 December 2021) with a three-month buffer period between the two time frames excluded. Four of the five pillars of the safe system had road safety indicators that worsened during the pandemic. Mortality was 19.7% higher for motor vehicle occupants and 45.1% higher for riders of motorized two-wheeled vehicles. In adjusted analyses, failure to use safety equipment (safety belts/helmets) was associated with 44% higher mortality. Two road user groups, non-motorized bicyclists and pedestrians, did not contribute significantly to higher mortality. Urban roadway crashes were higher compared to rural crashes. Additional scientific inquiry into factors associated with COVID-19-era mortality using the Safe System framework yielded important scientific insights to inform prevention efforts. Motorized two-wheeled vehicles contribute disproportionately to pandemic-era higher mortality and constitute an emerging road safety issue that deserves further attention. Full article

In the field of autonomous vehicle (AV) acceptance and opinion studies, questionnaires are widely used. Additionally, AV experiments and driving simulations are utilized. However, few AV studies have investigated social media, and fewer studies have analyzed the impact of AV crashes on public opinion, often relying on limited social media datasets. This study aims to address this gap by exploring a comprehensive dataset of six million tweets posted over a decade (2012–2021), and neural networks, sentiment analysis and knowledge graphs are applied. The results reveal that tweets predominantly convey negative sentiment (40.86%) rather than positive (32.52%) or neutral (26.62%) sentiment. A binary segmentation algorithm was used to distinguish an initial positive sentiment period (January 2012–May 2016) followed by a negative period (June 2016–December 2021), which was initiated by a fatal Tesla accident and reinforced by a pedestrian killed by an Uber AV. The sentiment polarity exhibited in the posted tweets was statistically significant (U = 24,914,037,786; p value < 0.001). The timeline analysis revealed that the negative sentiment period was initiated by fatal accidents involving a Tesla AV driver and a pedestrian hit by an Uber AV, which was amplified by the mainstream media. 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

Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian–vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors “Workday or not” (Not), “Signal control mode” (No signal and Other security facilities), and “Road safety attribute” (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels. Full article

In Hungary, around 14,000 to 15,000 traffic accidents result in personal injuries on public roads annually. Half of the individuals involved in fatal collisions are considered vulnerable pedestrians or cyclists. Our retrospective cross-sectional study reviewed the police and medical aspects of personal injury traffic accidents in our county. In 2023, there were 26 fatal accidents, 9 of which involved pedestrians, with 8 being at-fault pedestrians. Additionally, 920 traffic accident patients visited the emergency care department of the county hospital, with 68% of them having injuries that healed within 8 days. Full article

Introduction: Road Traffic Accidents (RTAs) are the leading cause of premature death in young people aged 5–29. Globally, 186,300 children aged 9 years and under die from RTAs each year. Romania had the highest mortality rate in children aged 0 to 14 for 2018–2020. This study aimed to assess the involvement of children aged 0–17 years in fatal RTAs by analyzing medico-legal autopsy records in a 5-year period at Timisoara Institute of Legal Medicine (TILM), Romania. Materials and Methods: A retrospective analysis of medico-legal autopsy records of road fatalities in children aged 0–17 years, from TILM in a 5-year period (2017–2021), was conducted. Results: Of all medico-legal autopsies in the 5-year period, 23 cases (5.8%) involved road fatalities in children aged 17 and under. Preschoolers accounted for 10 cases, followed by the age group 15–17 years (n = 9). Most children sustained fatal injuries as passengers (n = 13), followed by child pedestrians (n = 7). This research follows four representative cases, each being a different type of child road fatality regarding the type of road user, the age of the victim, and the involvement of other risk factors. Conclusions: Our findings emphasize the tragedy of road fatalities in children and the need to determine risk factors and prevention strategies to reduce the enormous global crisis involving these vulnerable victims. Full article