Search Results (222)

Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve safety and liveability. This study systematically evaluates the effectiveness of TCMs in reducing speed and improving safety outcomes on urban roads, following PRISMA 2020 guidelines. It encompasses the identification, screening, and synthesis of articles from the Scopus, ScienceDirect, and SpringerLink databases, published between January 2020 and February 2026. Risk of bias in the included studies was assessed qualitatively by the co-authors. The assessment was conducted independently, with discrepancies resolved through discussion. A total of 91 studies were included in the review. Evidence from field studies, driving simulator experiments, and analytical, simulation, and computation-based evaluations is reviewed and structured within a three-cluster taxonomy comprising physical and geometrical measures, regulatory and perceptual interventions, and digital and technological approaches. The synthesis indicates that physically self-enforcing measures yield the most consistent reductions in speed. At the same time, regulatory and digital interventions can deliver meaningful safety benefits when implemented at scale with credible governance. Perceptual and advisory measures show more varying and context-dependent effects. The evidence base is limited by heterogeneity in study designs, short-term evaluations, and inconsistent reporting across studies. Full article

Pulse-width-modulated (PWM) automotive headlights enhance nighttime event-based camera detection, yet systematic parameter optimization for vulnerable road user detection remains unexplored. This study evaluates PWM frequency, duty cycle, light distribution, ego-vehicle speed, and ambient lighting under European New Car Assessment Programme-inspired crossing scenarios for cyclist and pedestrian detection. Results establish performance ranging from substantial improvements to severe degradation relative to continuous illumination. Cyclist detection achieves robust performance with high-frequency modulation across light distributions, while low-frequency operation with low beam produces severe degradation through background noise accumulation. Pedestrian detection requires high beam with street lighting enabled; low beam universally fails regardless of modulation parameters. Limited parameter combinations achieve simultaneous improvements for both targets. Detection performs optimally on retroreflective surfaces, while low-reflectivity clothing limits capability, requiring target-specific optimization. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient light, forcing drivers to rely on roadway lighting for hazard recognition. Improving illumination arrangements can significantly reduce the likelihood of crashes. However, there are significant differences in the effects of illumination on drivers’ visual search ability at different vehicle speeds. Therefore, the collaborative matching of illumination and speed limits can effectively improve traffic efficiency and reduce the probability of nighttime accidents. In this paper, we establish a collaborative optimization model of illumination and speed limits at rural highway intersections that considers drivers’ visual recognition of VRUs. We then design an experiment with illuminance, vehicle speed, and VRU type/location as control variables to collect recognition distances, and finally analyze their effects to calculate speed limits under different illuminances. Results indicate that pedestrians and cyclists appearing from the left side are recognized 24.73% and 15.79% earlier than those from the right, suggesting that VRUs from the right side are more vulnerable. Additionally, the safety benefit of improving illumination on increasing speed limits gradually diminishes as illuminance rises. Therefore, determining the most suitable illumination and speed limit configuration requires a comprehensive evaluation of the cost–benefit relationship between lighting investments and the gains resulting from higher speed limits. Full article

This paper develops a shock-absorbing asphalt mixture for pedestrian pavements that mitigates the impact of normal walking on pedestrians’ bodies by incorporating crumb rubber from recycled tires to produce a soft mixture. This aims to reduce injuries to vulnerable road users, enable the rethinking of urban pavement designs, and address the major challenges facing societies, ultimately achieving more sustainable, resilient, and safer cities. To promote land sustainability, the designed asphalt mixture should be pervious, allowing water to infiltrate into the underlying soil. The development of the asphalt mixture followed an experimental methodology that involved formulating asphalt mixtures with conventional bitumen, polymer-modified bitumen, and bituminous emulsion. The shock-absorbing capability was evaluated by measuring the deformation of the asphalt mixture over time in response to a falling weight from a Light Falling Weight Deflectometer. Permeability capabilities were assessed through the permeability test. Subsequently, the asphalt mixture was characterized according to its macrotexture, friction, air void content, rutting resistance, and stiffness to assess its suitability as a walking surface material. Results indicate that increasing rubber content enhances deformation capacity and improves cushioning but reduces stiffness. Among the solutions, mixtures with polymer-modified bitumen and intermediate rubber content achieved the balance between impact attenuation and mechanical performance. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

Over the past decade, road safety research has experienced rapid development due to the rapid expansion of large crash databases, the adoption of artificial intelligence techniques, and the demand for proactive and predictive safety solutions. This study conducts a data-driven review of recent research trends in transport safety. It focuses on main domains including crash severity analysis, human factors, vulnerable road users (VRUs), spatial modeling, and artificial intelligence applications. A systematic search of the Scopus database identified 15,599 relevant scientific papers published between 2016 and 2025. After constructing this corpus, titles, abstracts, and keywords were preprocessed using a natural language pipeline. The analysis employed BERTopic, a transformer-based topic modeling framework. The analysis identified 29 distinct research topics, further synthesized into five major thematic areas: (1) crash severity and injury analysis, (2) driver behavior and human factors, (3) vulnerable road users, (4) artificial intelligence, machine learning, and computer vision in intelligent transportation systems, and (5) spatial analysis and hotspot detection. A notable increase in publications related to artificial intelligence and machine learning has been evident since 2020. The results show a transition from descriptive, post-crash studies to integrated, multimodal, predictive analysis. Overall, the findings reveal a paradigm shift in the field. This study also identifies ethical and economic issues associated with the use of artificial intelligence in intelligent transportation systems, including data management, infrastructure requirements, system security, and model transparency. The results signify a transition from intuition-based models to explainable, spatially explicit, and data-intensive models, ultimately facilitating proactive risk assessment and informed decision-making. Full article

With the rapid advancement of autonomous driving technology and the commercialization of Human–Machine Interface (HMI) services, camera-based systems for external environment perception are being extensively deployed. While comprehensive camera systems enhance safety and convenience, they simultaneously raise serious privacy concerns by collecting facial and biometric information of Vulnerable Road Users (VRUs) and passengers. Although facial anonymization technology has emerged as a key solution, the field currently faces a fundamental challenge: the absence of unified performance evaluation criteria. Existing studies employ disparate evaluation metrics, making objective inter-model comparison and performance verification difficult. This study proposes quantitative evaluation metrics and corresponding evaluation criteria that enable systematic and objective assessment of facial anonymization model performance. Through large-scale experiments, we developed quantitative evaluation metrics encompassing facial landmark variations, visual similarity, and re-identification prevention capability, and derived specific threshold values based on statistical methodologies. Furthermore, to validate the proposed evaluation criteria, we conducted systematic empirical assessments using models that adopt different technical approaches. The validation experiments showed that the evaluation criteria proposed in this study can be applied across models with distinct technical characteristics. This research is expected to contribute to resolving the heterogeneous evaluation criteria issues in existing studies by providing unified evaluation criteria. It may also support the development of privacy protection technologies in autonomous driving environments. Full article

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article

Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. This study developed an explainable machine learning framework to predict motor vehicle-involved pedestrian and cyclist crash occurrence at urban intersections using five years of crash, geometric, operational, and socioeconomic data from a large set of urban intersections. Five supervised machine learning algorithms were trained and evaluated, including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The evaluated models demonstrated strong predictive performance overall, with accuracies approaching 91% and high discriminative capability. In particular, the Binary Logistic Regression and Random Forest models achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.961 and 0.964, respectively. To enhance transparency, SHAP values were used to quantify the contribution of predictors and examine feature effects at both the global and local levels. The results indicate that roadway hierarchy, intersection markings, and total entering volume are among the most influential determinants of crash likelihood, while socioeconomic variables exhibit weaker but interpretable effects. Full article

A large number of vulnerable road users such as pedestrians continue to be injured or killed in road accidents every year, and active safety systems such as automatic emergency braking systems are expected to improve the situation. However, automatic emergency braking systems for pedestrians have been tested in a variety of real-world scenarios. The purpose of this paper is to obtain typical P-AEB test scenarios that can reflect the real and collision scenarios through real pedestrian–vehicle crash data. By using the k-means clustering algorithm based on local outlier detection, the intersection data and the straight-road data are clustered and analyzed separately, with five types of typical P-AEB straight-road test scenarios and seven types of typical P-AEB intersection test scenarios. By comparing with the existing test protocols, the test scenarios proposed in this paper have good coverage and authenticity, and can play a guiding role in the construction of specific P-AEB system test scenarios. Full article

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

Traffic accidents involving Vulnerable Road Users (VRUs) frequently occur at unsignalized intersections on Japanese community roads. To prevent such accidents, collision avoidance systems need to predict VRUs’ movements throughout the entire road network while explicitly handling uncertainty degraded by sparse observations and frequent route branching at intersections. Based on this motivation, this study proposes a Vehicle-to-Network (V2N)-based comprehensive safety framework for estimation of VRU movement and prediction of future intersection entry for community roads. The framework integrates estimation results provided from Roadside Edges and Vehicle Edges at a Central Server. In addition, road geometry from map information is incorporated as pseudo-observations into the estimation, and multiple route hypotheses are explicitly managed to represent route branching at intersections. For intersection-entry prediction, entry certainty is calculated by integrating a predicted distribution. For evaluation of the proposed framework, we conduct Monte Carlo simulations on simplified grid road networks. The results show that the proposed framework maintains conservative estimation under sparse observations and improves prediction when additional observation information from surrounding vehicles becomes available. Furthermore, a simulation-based case study using an actual community road-network geometry shows the feasibility of the proposed framework for cooperative collision avoidance on actual community roads. Full article

Pedestrian safety remains a major concern in rapidly urbanizing cities of developing countries, where road traffic crashes constantly involve vulnerable road users. In Lahore, Pakistan, pedestrian facilities such as signalized crossings often underperform due to limited awareness, inadequate design, poor maintenance, and weak enforcement. This study evaluates pedestrian awareness, perception, and compliance with pedestrian signals along the Mall Road Corridor, a busy urban arterial serving diverse socio-economic groups. Data were collected through a self-administered questionnaire survey, yielding 600 valid responses. Descriptive statistics, Pearson correlation analysis, ordinal logistic regression, and factor analysis were employed to examine the influence of socio-demographic characteristics and perceived infrastructural attributes on pedestrian behavior. Results indicate that gender, age, education, employment status, and income significantly affect compliance with pedestrian signals. Factor analysis identified seven latent constructs related to compliance behavior, safety perception, signal placement, traffic conditions, perceived importance, and user satisfaction. Only 43% of respondents demonstrated full awareness of pedestrian signals, and 54% reported regular or occasional use. The findings highlight that in this perception-based study, both infrastructural quality and perceived safety strongly shape pedestrian compliance, underscoring the need for targeted design improvements and enforcement measures to enhance pedestrian safety in developing urban contexts. Full article

This study proposes a modelling framework for simulating cyclist–vehicle interactions at urban intersections characterised by geometric constraints and variable visibility conditions. A Digital Model (DM) of the intersection geometry was developed in SUMO, complemented by a custom behavioural model calibrated using experimental trajectory data to capture cyclists’ and drivers’ perception–reaction and braking behaviour. These two components were combined to simulate scenarios with varying visibility conditions and perception-triggered braking responses in severe conflict situations. Results show that reduced visibility significantly reduces temporal safety margins, with over 50% of all simulated interactions yielding differential time-to-arrival (TTA₂) values below 2 s. Furthermore, obstructed conditions lead to higher- and more-dispersed relative crossing speeds (DV), typically increasing by 0.5–1.0 m/s compared to unobstructed conditions. Simulation data confirmed that clear visibility promotes anticipatory and adaptive user behaviour, whereas limited sightlines reduce braking availability and increase the likelihood and severity of conflicts, with distributions conditioned by the intersection’s geometry. The ability to generate detailed synthetic datasets of cyclist–vehicle interactions, often not obtainable through field observation, demonstrates the potential of the proposed framework for safety assessment. This approach supports the evaluation of mitigation strategies, including C-ITS-based solutions, and provides a basis for developing predictive AI models to enhance the safety of vulnerable road users. Full article