Search Results (239)

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

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. 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

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

Seatbelts are critical components of vehicle safety, continuously evolving through technological advancements and regulatory updates. Traditionally designed to secure occupants during collisions, seatbelt innovations, such as retractors, pretensioners, and load limiters, have significantly enhanced comfort and effectiveness. With the advent of autonomous vehicles, seatbelt systems must adapt to new safety challenges, including real-time tension adjustment through active seatbelt systems. These systems, integrated with active safety technologies like automatic emergency braking, offer a more comprehensive safety solution. Furthermore, seatbelt technology must address the diverse needs of different passenger categories. Quantitative data highlight the role of seatbelts for various passenger categories. Children are 55% more likely to be injured by rear structure intrusion and 27% more likely to suffer from compression into the front seat during rear impacts. Pregnant women generally experience milder injuries but are more prone to abdominal injuries. Older adults, who account for 17% of crash fatalities, are more likely to suffer thoracic injuries and fractures due to increased bone fragility. This review explores the integration of traditional and modern seatbelt systems, focusing on passenger-specific adaptations and the future role of seatbelts in autonomous vehicles. This study is based on a thorough literature review, analyzing data from the Web of Science, Scopus, and SAE databases, where available, to assess the contributions and impact of these innovations. 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

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

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

Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing studies related to road vehicle accidents, reconstruction methodologies, assessment of vehicles crashworthiness, as well as evaluation of occupants’ behavior in different collision scenarios. Therefore, the present paper aims to offer a comprehensive overview of the specialty literature approaches in terms of road vehicle accidents through an analysis of the reconstruction methods used in the cases of vehicle-to-vehicle or vehicle-to-object crashes, as well as ways in which the crashworthiness of road vehicles is assessed by specialized organizations or individual experts. The addressed topics were summarized from a range of European and global strategies in the field of transportation, reports, testing protocols, as well as scientific research papers published in international databases. The main purpose of the present paper is to serve as a foundational resource for researchers and practitioners seeking to contextualize their work within a global framework. Full article

Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rely on the content and quality of accident datasets. To increase the robustness and accuracy of prediction models, this paper leverages a Transformer-based architecture for the severity prediction of traffic collisions from human injury severity. This framework learns both text and sequence data from accident datasets. After comparative analysis, the framework can achieve the prediction of human injury severity under different data categories and show good prediction performance at low injury severity levels using only textual data or sequence data. Full article

Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called Road Intersection Traffic Crash Prediction (RoadInTCP), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called topological-relational features (tr-features), of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to tr-features which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed RoadInTCP model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed RoadInTCP robustly outperforms existing methods. Full article

Understanding the cultural and environmental influences on roadway crash patterns is essential for designing effective prevention strategies. This study applies advanced AI techniques, including Bidirectional Encoder Representations from Transformers (BERT) and Shapley Additive Explanations (SHAP), to examine traffic crash patterns in the United States and Jordan. By analyzing tabular data and crash narratives, the research reveals significant regional differences: in the USA, vehicle overturns and roadway conditions, such as guardrails, are major factors in fatal crashes, whereas in Jordan, technical defects and driver behavior play a more critical role. SHAP analysis identifies “driver” and “damage” as pivotal terms across both regions, while country-specific terms such as “overturn” in the USA and “technical” in Jordan highlight regional disparities. Using BERT/Bi-LSTM models, the study achieves up to 99.5% accuracy in crash severity prediction, demonstrating the robustness of AI in traffic safety analysis. These findings underscore the value of contextualized AI-driven insights in developing targeted, region-specific road safety policies and interventions. By bridging the gap between developed and developing country contexts, the study contributes to the global effort to reduce road traffic injuries and fatalities. 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