Search Results (288)

The increasing incidence of traffic accidents involving elderly pedestrians has highlighted the necessity for effective strategies to improve visibility in low-light environments. Conventional safety garments based on retroreflective materials or optical fibers exhibit limitations, including passive operation and low luminance. In this study, a textile-based organic light-emitting diode (OLED) safety garment with automatic light-sensing functionality is proposed to overcome these limitations. The OLED devices were fabricated on an ultrathin polyethylene terephthalate (PET) substrate and transferred onto a textile substrate to maintain flexibility and wearability. A light-emitting module incorporating a LilyPad Arduino and ambient light sensor was implemented to enable automatic illumination under low-light conditions. The fabricated textile-based OLED exhibited a luminance of 550 cd/m² at 4.5 V and maintained stable performance after transfer, with a T₅₀ lifetime of 485 h. Thermal analysis showed a minimal temperature increase of 2.9 °C after 5 h of operation, remaining below body temperature. Moreover, mechanical testing confirmed over 95% luminance retention after 2,000 bending cycles. The fabricated OLED-based luminous safety garment exhibited lightweight wearability with a total weight of 140 g and improved visibility at observation distances of up to 50 m under low-light conditions. These results indicate that the proposed OLED-based luminous safety garment can offer a viable solution for enhancing the safety of elderly pedestrians. Full article

Improving pedestrian safety at urban intersections is a key challenge for achieving safer and more sustainable urban transport systems. This study develops a multi-criteria decision-making model (MCDM) for selecting the most appropriate traffic signal type at pedestrian crossings in different urban zones. Traffic conditions, illegal pedestrian crossings and the number of traffic accidents were taken into account during the modelling, as well as the characteristics of the urban environment. The research involved 66,616 pedestrians at 22 pedestrian crossings located in three urban zones: school zones, central zones, and non-central zones. The data were aggregated using Bayesian (beta-binomial) and classical statistical methods. The OPA-Group method was then used to develop the model. In the decision-making phase, the Ordinal Priority Approach (OPA) was applied as the core MCDM method. It was then extended to the OPA-Group framework to incorporate group-based evaluation in accordance with the model requirements. Additionally, a comprehensive sensitivity analysis was conducted, confirming the robustness and stability of the proposed model. The results show that traditional traffic signals are most suitable for school and non-central zones, whereas countdown traffic signals are recommended for central zones. Push-button traffic signals were identified as the least efficient solution for regulating pedestrian movement at pedestrian crossings. Full article

Electric vehicles introduce new considerations for pedestrian safety because their lower operating noise at low speeds may reduce pedestrian detectability in urban traffic environments. This study proposes a simulation-based integrated active–passive pedestrian protection framework for electric vehicles by linking automatic emergency braking, active hood deployment, and post-crash head injury assessment. A total of 688 valid pedestrian–vehicle crash records from the National Highway Traffic Safety Administration database were analyzed, and 5 representative pedestrian crash scenarios were constructed through clustering-informed scenario screening and a benchmark pedestrian AEB scenario. The scenarios were reconstructed in a PreScan–Simulink co-simulation environment to evaluate a time-to-collision-based AEB strategy, while the active hood system was assessed using multi-body dynamics simulation and finite element head impact analysis. The AEB results showed that three scenarios were avoided before pedestrian contact, whereas two remained unavoidable, with residual impact speeds of approximately 31.5 km/h and 46 km/h. The hood reached a stable deployed posture within approximately 0.1 s under the modeled conditions. The HIC₁₅ results at eight selected impact points showed that speed reduction and hood deployment generally reduced head injury metrics, but full compliance with the reference HIC₁₅ threshold of 1000 was not achieved at all points. These findings suggest that the proposed strategy can improve simulated pedestrian head protection performance under selected electric vehicle crash scenarios, while further structural optimization, experimental validation, and cost–benefit assessments are still required. Full article

In mixed pedestrian–vehicle traffic environments, overtaking pedestrians by vehicles is a prevalent and complex human–vehicle interaction scenario. However, this maneuver often leads to accidents, resulting in injuries and fatalities, primarily due to inadequate in frastructure, limited pedestrian safety awareness, and suboptimal driver behavior. To mitigate such accidents and develop active vehicle safety systems and autonomous driving algorithms based on human–vehicle interaction data, it is crucial to investigate the overtaking behavior of human drivers. This study examines driver overtaking behavior under various conditions through driving simulator experiments and evaluates how different experimental variables influence driver performance. Using data from 12 skilled drivers, a risk corridor for vehicles overtaking pedestrians is established and a lateral distance prediction model is developed. Based on this established risk corridor, a vehicle overtaking strategy is proposed. Furthermore, to assess the risk level associated with overtaking pedestrians, pedestrians’ subjective risk perceptions are quantified. The simulation results indicate that the maximum lateral error of the vehicle is approximately 0.14 m, the maximum heading error is about 0.06 radians, and the vehicle’s trajectory during pedestrian overtaking remains within the defined risk corridor. These findings are consistent with the operational characteristics of human drivers. 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

Road traffic injuries represent one of the most critical public health challenges in the Gulf region. Predicting traffic accident severity is therefore a critical component of evidence-based road safety management. In this study, we develop machine learning frameworks for predicting traffic accident severity using Qatar’s national dataset (2020–2025), addressing extreme class imbalance and interpretability. A dataset of 588,023 accident records was systematically preprocessed from 1,000,500 raw reports. We compare three approaches: multi-class (four severity levels), binary (Safe vs. Severe), and cascaded two-stage (combining both). Six classifiers were evaluated across two encoding methods and three balancing strategies. Systematic hyperparameter tuning with 5-fold stratified cross-validation was performed for all models. The binary LightGBM classifier achieved BA = 71.04%, AUC-ROC = 0.772, Sensitivity = 61.03%, and Specificity = 81.05%, demonstrating superior performance over multi-class approaches. Temporal validation on 2025 data (trained on 2020–2024 data) supported good temporal generalization. Analysis of 10,000 test instances identified the time period as the dominant predictor of accident severity. The binary LightGBM framework provides an interpretable and effective approach for severe accident identification and risk prioritization, with SHAP findings supporting targeted temporal enforcement and pedestrian safety as evidence-based policy priorities. Full article

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The proposed method in this research involves the bidirectional Long Short Term Memory (LSTM), Convolutional Neural Network with LSTM (CNN–LSTM), and transformer models. The model is furthermore tuned using random or grid search. For the pedestrian–vehicle scenario, the CNN–LSTM model achieved 99.76% accuracy, 99.77% precision, and 99.76% recall, highlighting its strong classification performance. In the vehicle–motorcyclist scenario, the bidirectional LSTM reached 99.73% accuracy with precision and recall of 99.15%, demonstrating its effectiveness in detecting imminent crashes. The optimized CNN-LSTM by random search has focused on decreasing the false-positive rate and increasing the positive rate. It has achieved superior results compared to previous research. These results suggest that the system could be effectively implemented as an early collision warning solution on edge devices. Full article

Rapid urbanization and increasing traffic volumes necessitate effective road safety measures, particularly in metropolitan areas. Enhancing road safety is a fundamental pillar of social sustainability as it directly reduces the socio-economic burden of traffic accidents and promotes resilient urban environments. This article analyzes the impact of infrastructural traffic-calming devices on road safety parameters using a GIS-based method. This study provides a quantitative tool for monitoring and measuring the effectiveness of sustainable transport infrastructure. The study examines six different types of devices across 44 locations within the GZM Metropolis, Poland, utilizing official police data (Accident and Collision Records System—SEWIK) from a period of two years before and two years after implementation. The primary parameters analyzed include the frequency of incidents, the severity of injuries, and the structure of accident types. The results demonstrate a substantial positive association following the interventions, with an average 41.33% reduction in road incidents across all tested devices. Specifically, speed bumps proved most effective, reducing incidents by over 66%. However, the analysis revealed a critical anomaly: While pedestrian refuge islands decreased the overall number of minor injuries, they correlated with an increase in the number of severe injuries, suggesting a need for careful consideration. Furthermore, the study confirms a positive shift in the structure of incidents, notably a substantial decrease in rear-end and side-impact collisions. The findings offer practical evidence for evidence-based urban policies, contributing to the development of safe, inclusive, and sustainable transport systems in line with global sustainability goals. Full article

Road traffic accidents remain a major public health concern, and effective safety management requires interpretable tools that integrate multiple causal dimensions. This study proposes a Traffic Accident Causation Index (TACI) to provide a holistic representation of severity-related drivers by combining six theoretically grounded domains: Accident Infrastructure, Driver, Pedestrian, Road Condition, Emergency and Response, and Severity. Using a national police-reported dataset from Türkiye (N = 13,639), operational variables are mapped to normalized risk scores, aggregated into domain indices, and combined into a 0–100 composite TACI score. To assess the robustness and compatibility of the proposed index framework, we develop ensemble machine learning models (Random Forest, Gradient Boosting, LightGBM, XGBoost, and CatBoost) under two feature configurations: an Extended Feature Set (EFS) with the original variables and a Core Feature Set (CFS) consisting of the six domain indices. The results indicate that domain-level aggregation improves predictive stability, and the best-performing boosting models (XGBoost/CatBoost) achieve near-perfect agreement with the constructed index (test R² > 0.99) and very high classification performance (AUC > 0.999). SHAP-based explainability highlights pedestrian exposure and vulnerability as the dominant contributors, followed by lighting/visibility conditions, road surface quality, and adverse road–environment factors, whereas emergency-response and infrastructural attributes show comparatively indirect effects. Overall, the proposed framework supports interpretable, domain-oriented evidence for prioritizing safety interventions and monitoring high-risk accident conditions. Full article

Real-world traffic is highly dynamic, with pedestrians exhibiting unpredictable movements. Pedestrians’ poses are essential cues for predicting their actions, enabling vehicles to respond proactively and reduce accident risks. In autonomous driving, the distance between vehicles and pedestrians is critical, making 3D human pose estimation crucial. In this context, pedestrian pose estimation has been actively studied, and recently, light detection and ranging (LiDAR) sensors have attracted attention due to their accurate 3D depth information and privacy benefits. However, existing LiDAR-based 3D pose estimation methods mainly process 3D data directly, requiring high computational cost and memory. In this paper, we propose a lightweight LiDAR-based 3D human pose estimation method specifically designed for deployment in autonomous driving systems. Unlike conventional 3D direct processing methods, our approach strategically reduces computational complexity by projecting point clouds into 2D depth images and leveraging a lightweight MoveNet, followed by efficient 3D lifting. Furthermore, we introduce a self-occlusion correction algorithm to improve robustness under side-view and bending poses, where depth-based projections often suffer from distortion. Experimental results on benchmark datasets demonstrate that the proposed method achieves competitive pose estimation accuracy while substantially improving efficiency, highlighting its practicality and scalability for real-time autonomous vehicle applications. 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

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that the total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function $\beta silu$, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

The accurate simulative reconstruction of blind spot accidents requires innovative simulation methods. The objective of this paper is to analyze the avoidability of a specific blind spot accident and assess the impact of various parameters as if an active turning assistant had been installed in the truck. Additionally, it proposes a novel adaptation of the turning assistant system, along with an adapted simulation model tailored for drawbar trailers. The analyses presented in this paper were performed using PC-Crash accident simulation software, applying the “Active Safety” module. After performing a simulation of an accident involving a right-turning truck with a center axle trailer and a pedestrian, the avoidability of the accident was examined by simulating the scenario as if the truck involved in the accident had been equipped with an active turning assistant system. Subsequently, a parameter analysis was conducted to analyze the effect of changes in the active turning assistant’s parameters and changes in the pedestrian’s direction of entry on the avoidability of the accident. In doing so, we determined the parameters for the worst-case (collision) and the best-case (no collision) scenarios. Finally, an adaptation and further development of the active turning assistant, along with a corresponding simulation method for drawbar trailers, are proposed. Full article

Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics. Full article