Traffic Injury Prevention最新文献

{"title":"Attitude of parents on the use of car seats for their children in Riyadh, Saudi Arabia.","authors":"Khalid A Abalkhail, Mohammed Aldabeis, Laila Alghamdi, Rayan Almutairi, Saud Alhindi","doi":"10.1080/15389588.2025.2561773","DOIUrl":"https://doi.org/10.1080/15389588.2025.2561773","url":null,"abstract":"<p><strong>Objectives: </strong>Road traffic accidents (RTAs) are the leading cause of death among children globally. According to motor vehicle collision reports from the Saudi Ministry of Health, 3,202 children younger than 18 years were involved in RTAs in 2020, resulting in 587 fatalities. Moreover, the World Health Organization reports that the use of child restraint systems (CRS) can reduce infant deaths by up to 71% globally. This highlights that a major factor influencing the outcome of an RTA is the use of CRS. This study aimed to assess parents' attitudes and awareness regarding the use of CRS, including their perceived benefits and barriers.</p><p><strong>Methods: </strong>This cross-sectional study was conducted among parents in Riyadh, Saudi Arabia, with children from birth to 12 years old. A self-administered questionnaire was distributed among the targeted parents using an online survey. Of the 468 parents, 50.4% were male and 32.7% were between 26 and 35 years old. The questionnaire includes sociodemographic characteristics, a questionnaire to assess parents' practices on using CRS, and a 5-item questionnaire to assess parents' attitudes toward the CRS.</p><p><strong>Results: </strong>Only 34.8% of parents reported having a CRS. The most common barriers to CRS usage were financial constraints and lack of conviction about their importance. Approximately 46.2% of respondents exhibited a positive attitude toward using CRS. The most significant predictors of positive attitudes were higher educational attainment and having a CRS available in the vehicle.</p><p><strong>Conclusions: </strong>Parents in Riyadh demonstrated a generally favorable attitude toward the use of CRS. However, those with lower education levels and without access to a CRS in their vehicle were more likely to have negative attitudes. Promoting positive attitudes toward CRS may enhance child safety during travel. Therefore, targeted educational campaigns are essential to raise awareness and encourage consistent use of CRS among parents in the region.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-6"},"PeriodicalIF":1.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spiral tunnel driving and cognitive load: An eye-tracking investigation into tunnel geometry and traversal effects.","authors":"Lei Han, Huimin Zhou, Pengsen Gu, Zhigang Du","doi":"10.1080/15389588.2025.2561772","DOIUrl":"https://doi.org/10.1080/15389588.2025.2561772","url":null,"abstract":"<p><strong>Objectives: </strong>Despite the increasing construction of spiral tunnels, the specific scientific problem of how their unique geometric characteristics (length and radius) and travel direction (upward vs. downward) collectively influence drivers' cognitive load remains insufficiently understood, with a lack of systematic quantification using eye movement metrics. This study aimed to evaluate and quantify the cognitive load experienced by drivers navigating spiral tunnels, focusing on addressing this gap by examining the effects of tunnel geometry and travel direction through eye movement metrics.</p><p><strong>Methods: </strong>A naturalistic driving experiment was conducted with 30 licensed drivers in three spiral tunnels varying in length (1,330, 2,200, and 4,460 m) and radius (1,000, 850, and 700 m). Eye movement data, including fixation duration, pupil diameter, saccade duration, and saccade amplitude, were collected and analyzed to assess cognitive load.</p><p><strong>Results: </strong>Increased tunnel length and decreased radius have been associated with greater cognitive load. Specifically, the average fixation duration in the 4,460-m-long, 700-m-radius Hankou Tunnel is 4.6% higher than that in the 2,200-m-long, 850-m-radius Liuyuan Tunnel and 12.9% higher than in the 1,330-m-long, 1,000-m-radius Nanping Tunnel. The average pupil diameter in the Hankou Tunnel is 3.5% larger than that in the Liuyuan Tunnel and 7.7% larger than in the Nanping Tunnel. The average saccade duration in the Hankou Tunnel is 14.8% longer than that in the Liuyuan Tunnel and 34.0% longer than in the Nanping Tunnel, while the average saccade amplitude in the Hankou Tunnel is 5.0% smaller than that in the Liuyuan Tunnel and 14.0% smaller than in the Nanping Tunnel. Drivers have also experienced higher cognitive load during upward traversal compared to downward traversal, with the average fixation duration during upward traversal being 10.0% higher than that during downward traversal, the average pupil diameter during upward traversal being 4.0% larger than that during downward traversal, the average saccade duration during upward traversal being 10.6% longer than that during downward traversal, and the average saccade amplitude during upward traversal being 5.5% smaller than that during downward traversal. No significant interaction effects have been observed among tunnel length, radius, and travel direction on the eye movement metrics.</p><p><strong>Conclusions: </strong>Optimizing tunnel design is crucial to minimizing cognitive demands on drivers. Shorter and wider tunnels are recommended, and design features such as enhanced lighting and improved signage should be considered to mitigate the additional cognitive load of uphill driving. These findings have significant implications for enhancing driver safety and performance in spiral tunnels.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-12"},"PeriodicalIF":1.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle trajectory-based prediction of traffic conflicts on sharp horizontal curves.","authors":"Hao Li, Xiaofei Zhang","doi":"10.1080/15389588.2025.2566188","DOIUrl":"https://doi.org/10.1080/15389588.2025.2566188","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The traffic conflict situations at sharp curve sections are evaluated by analyzing vehicle trajectory data during navigation through these hazardous road segments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study develops a methodology for quantifying traffic conflict probabilities in curve scenarios based on multi-source trajectory data acquisition. Vehicle movement trajectories through curves are captured &lt;i&gt;via&lt;/i&gt; integrated UAV aerial photography systems and onboard vehicle recorders. High-precision spatiotemporal coordinates with dynamic parameters (instantaneous velocity and acceleration) are extracted using the professional trajectory analysis software. To address noise interference in raw trajectory data, a Kalman filtering algorithm is implemented for optimal motion state estimation and data smoothing. At the model architecture level, we propose a CNN-LSTM hybrid predictive model that synergistic-ally combines the spatial-temporal feature extraction capabilities of convolutional neural networks with the temporal dependency modeling advantages of long short-term memory networks, enabling end-to-end learning for quantitative trajectory conflict prediction. To validate model generalizability, this study concurrently constructed multiple benchmark models-including Support Vector Machine (SVM), Gradient Boosted Trees (XGBoost), GNN-LSTM, Vanilla LSTM, and Bi-LSTM-for comparative experiments. The evaluation framework employed a rigorous multi-dimensional validation protocol from machine learning, assessing all models not only by fundamental classification accuracy but also through fine-grained efficacy metrics (Precision, Recall, F1-score). Results demonstrated the superior performance of the hybrid CNN-LSTM model in predicting traffic conflicts at curves. Ultimately, curve-specific conflict probabilities were derived by applying the CNN-LSTM model to experimental data analysis. The generalization performance under class-imbalanced conditions was quantified using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), while prediction accuracy was validated through metrics including classification accuracy. This establishes a comprehensive multi-capability evaluation framework covering model stability, sensitivity, and generalization capability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Empirical results confirm the CNN-LSTM model's superior performance in sharp-curve conflict prediction, achieving a mean accuracy exceeding 85%, precision above 82.7%, recall over 89.9%, and F1-score surpassing 86.1%, complemented by a 93.5% or higher average AUC-ROC that demonstrates robust generalization in class-imbalanced scenarios. These metrics collectively substantiate its exceptional spatiotemporal feature extraction capability and precise risk evolution pattern fitting, enabling enhanced representation of interactive vehicle conflicts in complex environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The research outcomes provid","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation into road safety performance in Chinese provincial-level administrative regions: Insights from the input-output analysis.","authors":"Liangguo Kang","doi":"10.1080/15389588.2025.2563595","DOIUrl":"https://doi.org/10.1080/15389588.2025.2563595","url":null,"abstract":"<p><strong>Objectives: </strong>Road safety is a vital public concern aimed at preventing road-related injuries and fatalities. Assessing road safety status can be effectively conducted using performance measurement tools, which help identify areas for improvement and guide the development of targeted safety strategies.</p><p><strong>Methods: </strong>This study evaluates the road safety performance of Chinese provinces from 2018 to 2020 using an undesirable data envelopment analysis model and the meta-frontier approach to measure input-output efficiency.</p><p><strong>Results: </strong>During the three-year period, two, three, and two provinces, respectively, achieved efficiency scores considered as benchmarks. Clustering analysis grouped the performance into three tiers, with Beijing and Shanghai consistently in the highest-performing tier. Provinces in eastern China demonstrated relatively stable and high performance across all years. Meanwhile, Guangxi, Qinghai, and Guizhou showed potential for reducing undesirable output by over 80% annually. Changes in scores for 20, 19, and 20 provinces were driven by differences in production frontier technologies over time. A comparative evaluation incorporating desirable output offers an additional perspective on performance measurement.</p><p><strong>Conclusions: </strong>This research presents a framework for assessing road safety performance using both undesirable and desirable outputs and offers insights for policymakers to understand regional safety variations under diverse economic contexts.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-12"},"PeriodicalIF":1.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beliefs and knowledge about road safety education among kindergarten principals and teachers in Argentina: a qualitative study.","authors":"Jeremías David Tosi, Natalia Alejandra Minjolou, Carlos M Díaz-Lázaro, Rubén Daniel Ledesma, Fernando Martín Poó","doi":"10.1080/15389588.2025.2566177","DOIUrl":"https://doi.org/10.1080/15389588.2025.2566177","url":null,"abstract":"<p><strong>Objective: </strong>Road safety education is essential for preventing traffic injuries and promoting safe behavior from an early age. In this regard, principals and teachers play a key role. This qualitative study explored opinions and practices associated with early childhood road safety education, from the principals and teachers' perspective.</p><p><strong>Methods: </strong>The study took place in a city in Argentina (Mar del Plata), where 37 in-depth interviews were conducted with teachers and principals from 13 institutions located in different areas of the city. The following general dimensions were explored: knowledge of regulatory frameworks and road safety guidelines; road safety training; conceptions of road safety education; the role of preschools, parents, and local government; and perceived difficulties in implementing actions.</p><p><strong>Results: </strong>The results showed a low level of knowledge about general road safety regulations and guidelines for children. Two main conceptions of road safety education were identified: a normative approach, focused on obeying rules, and a comprehensive approach, more oriented toward self-care, social protection, and the environment. Principals and teachers emphasized the importance of informational and practical activities and identified a lack of time, resources, training, and government support as the main barriers. They identified the need to involve families, adapt content to the local context, and coordinate efforts with other educational levels.</p><p><strong>Conclusion: </strong>The effective implementation of early childhood road safety education depends not only on developing a regulatory framework, but also on improving the knowledge and practices of principals and teachers, establishing comprehensive policies, coordinating the participation of stakeholders, and incorporating supportive measures such as monitoring and oversight.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal analysis of injury severity of two-vehicle collision crashes: Insight from a correlated random parameters approach.","authors":"Hua Liu, Yongfeng Ma, Tiezhu Li","doi":"10.1080/15389588.2025.2561765","DOIUrl":"https://doi.org/10.1080/15389588.2025.2561765","url":null,"abstract":"<p><strong>Objective: </strong>Two-vehicle collision crashes are always tough challenges for traffic management departments due to its severe consequences. This study aims to investigate risk mechanism of three types of two-vehicle collisions (i.e., head on, sideswipe, and rear end) in the same city from a comprehensive perspective.</p><p><strong>Methods: </strong>A random parameters binary logit framework was employed to capture the unobserved heterogeneity across individual observations and reveal potential correlations between risk indicators of injury severity.</p><p><strong>Results: </strong>The results indicate that the correlated random parameters model performs best, and the impacts of risk indicators involving unsafe driving behavior and driver, vehicle, roadway, environment, and temporal characteristics on two-vehicle collisions are quite different. Based on the determinants of each two-vehicle collision, some recommendations have been proposed to improve the level of road traffic safety.</p><p><strong>Conclusions: </strong>Fatal collisions are more likely to happen when driving on the roads with higher road function classification and involving the presence of heavy trucks. Road type, weather condition, and unsafe driving behavior are primary contributors to two-vehicle collisions. Besides, fatal head-on collisions are prone to occurring when exceeding speed limits and driving on rainy days. Illegal overtaking or lane changing significantly increases the risk of fatal injuries in both sideswipe and rear-end collisions. Moreover, significant correlations between the on-ramp of highways and violating traffic lights or signs, and between cloudy days and visibility range within 50-100 m, are identified in the injury severity models of sideswipe and rear-end collisions, respectively. Current findings suggest that more attention should be devoted to intervening unsafe driving behaviors for sideswipe collisions, as well as enhancing the provision of visibility range information to mitigate rear-end collisions in adverse weather conditions.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving risk variation in mountainous highway tunnels of different lengths: Field evidence from Chongqing, China.","authors":"Yunwei Meng, Jiaqi Hu, Jiajun Shen, Binbin Li, Wenshi Zheng, Guangyan Qing, Fang Chen","doi":"10.1080/15389588.2025.2560531","DOIUrl":"https://doi.org/10.1080/15389588.2025.2560531","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to reveal the spatial distribution characteristics of driving risks in two-lane mountainous highway tunnels, with a particular focus on the influence of different tunnel lengths on risk levels, thereby contributing to improved tunnel operational safety.</p><p><strong>Methods: </strong>Field driving tests were conducted in 21 short, medium, and long tunnels located on two-lane highways in Chongqing, China. Multisource data were collected from 27 drivers, including heart rate growth rate, speed, illuminance change rate, and alignment complexity indices. The entropy-weighted method was used to determine the weights of various risk evaluation indicators, which were then integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to compute the comprehensive risk value for each tunnel. Risk levels were classified into low, relatively high, and high using the K-means clustering algorithm to analyze spatial distribution patterns.</p><p><strong>Results: </strong>The study showed that short tunnels exhibited the highest overall risk level, while long tunnels had the lowest. All three tunnel types displayed a consistent pattern, which is that entrance zones exhibited significantly higher risk than exit zones, with the lowest risk occurring in the middle segments. Specifically: (1) For short tunnels, the peak risk appeared 21 m after the entrance, with high-risk zones extending up to 144 m; (2) For medium tunnels, high-risk spans were concentrated within 50-75 m before and after the entrance, with the exit zone presenting the second-highest risk; (3) For long tunnels, the peak risk was found 2 m after the entrance, and both entrance and exit zones had significantly elevated risk. The average risk value in entrance segments was approximately 1.5 times that of the middle segments.</p><p><strong>Conclusions: </strong>Driving risks in two-lane highway tunnels exhibit distinct spatial distribution characteristics, with tunnel entrances and exits being the most risk-prone zones. Short tunnels, due to the frequent transition effect, present more pronounced risks. The findings provide theoretical support for tunnel structural design optimization, speed limit, and lighting system.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing traffic safety by forecasting the severity of road accident injuries using pyramidal dilation attention convolutional networks designed by the reptile search algorithm.","authors":"Nanditha Boddu, Venkata Ramana K, Ramesh Cheripelli","doi":"10.1080/15389588.2025.2549886","DOIUrl":"https://doi.org/10.1080/15389588.2025.2549886","url":null,"abstract":"<p><strong>Objective: </strong>This work aims to give a method that is both efficient and comprehensible for forecasting the extent of injuries sustained in traffic accidents. This addresses the limitations of existing GNN-based frameworks, which often struggle with complexity, limited interpretability, scalability issues, and the need for extensive data pre-processing and advanced graph representation learning.</p><p><strong>Methods: </strong>In this manuscript, Predicting Road Crash Injury Severity utilizing Pyramidal Dilation Attention Convolutional Network optimized with Reptile Search Algorithm (PRCIS-PDACN-RSA) is proposed. Firstly, the input data is gathered from the UK road accident dataset. The data is then sent to pre-processing, where the Robust Maximum Correntropy Kalman Filter (RMCKF) is applied to eliminate null, noisy, or incomplete entries. The pre-processed data is fed into Adaptive SV-Borderline SMOTE (ASV-SMOTE) to balance the imbalanced dataset. Then the balanced dataset is given to the Pyramidal Dilation Attention Convolutional Network (PDACN) to predict road crash injury severity and classify it as either severe or non-severe. The Reptile Search Algorithm (RSA) is used to optimize the PDACN parameters, enhancing its predictive performance.</p><p><strong>Results: </strong>The proposed PRCIS-PDACN-RSA technique is implemented in Python and evaluated using performance metrics, including accuracy, F1-score, recall, precision, Receiver Operating Characteristic (ROC), and Matthews's correlation coefficient (MCC), to assess its efficiency. The proposed PRCIS-PDACN-RSA approach attains 97.2% accuracy, 0.90% MCC, and 98.11% recall compared with existing methods, including Road Crash Injury Severity Prediction Utilizing a Grey Wolf Optimization-driven Artificial Neural Network for Predicting Road Crash Severity (GWO-ANN-PRCS), Graph Neural Network Framework (RCI-SP-GNN), and Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction (MGCN-TARP).</p><p><strong>Conclusions: </strong>The results demonstrate that the proposed PRCIS-PDACN-RSA framework outperforms existing methods in predicting road crash injury severity. Its high accuracy, robustness, and efficient handling of pre-processing and optimization highlight its suitability for real-world intelligent traffic safety systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helmet feature preferences and willingness to pay among iranian motorcyclists: A discrete choice experiment.","authors":"Marziyeh Najafi, Hoorieh Hallajian, Mahsa Eshik Aghasi, Mostafa Golshekan, Morteza Rahbar-Taramsari, Ali Davoudi Kiakalayeh, Enayatollah Homaie Rad","doi":"10.1080/15389588.2025.2554855","DOIUrl":"https://doi.org/10.1080/15389588.2025.2554855","url":null,"abstract":"<p><strong>Objectives: </strong>Helmets are vital for motorcyclists. They prevent death and head injuries. Researchers have found many barriers to motorcyclists' helmet use, one of which is related to helmet features. Studying motorcyclists' choice of helmet features can reduce these barriers.</p><p><strong>Methods: </strong>In this choice experiment, 250 motorcyclists in Rasht, Iran, were surveyed using convenience sampling in 2023. Motorcyclists were presented with 14 choice sets with two scenarios about helmet features. They were asked to choose between the two helmets which is more aligned with their preferences. Attributes of scenarios were selected in a qualitative study and literature reviews. These attributes included: price, rigidity of the outer helmet, and being full face, flip front, or open face. Also, the internals must be washable, and the helmet's weight. Data were analyzed using conditional logistic regression.</p><p><strong>Results: </strong>Participants did not prefer a higher price (ß = -0.270 ± 0.025), an open face (ß = -0.463 ± 0.082), a weighted design (ß = -1.970 ± 0.060), non-washable internal parts (ß = -0.183 ± 0.060), and poor rigidity of external parts (ß = -1.977 ± 0.086) of helmets. The preferences were different among wealth, education, and age subgroups.</p><p><strong>Conclusions: </strong>To boost helmet use among motorcyclists, policymakers must use market fragmentation techniques and subsidize helmets in different subgroups. This intervention can work with health campaigns and fines for not wearing proper helmets.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-7"},"PeriodicalIF":1.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HR-YOLO: Segmentation and detection of emergency escape ramp scenes using an integrated HR-net and improved YOLOv12 model.","authors":"Guiling Li, Zuosheng Hu, Haozhi Zhang","doi":"10.1080/15389588.2025.2557513","DOIUrl":"https://doi.org/10.1080/15389588.2025.2557513","url":null,"abstract":"<p><strong>Objective: </strong>With the development of intelligent transportation systems, the demand for automatic recognition and monitoring of critical road safety infrastructure has been increasing. Particularly in high-risk road sections such as mountainous areas and steep downhill stretches, Emergency Escape Ramps (EERs) play a crucial role in preventing severe accidents caused by out-of-control vehicles. Accurate detection of these ramps is essential for enhancing road traffic safety.</p><p><strong>Methods: </strong>To address the limitations of existing methods, such as inadequate segmentation accuracy and poor robustness in object detection, this paper proposes a new model (HR-YOLO) that integrates the HR-Net model with an improved YOLOv12 model. To support the segmentation and detection of emergency escape ramp scenarios, we constructed a custom dataset consisting of 411 annotated images across 8 categories. This dataset reflects typical road environments and object types encountered in emergency ramp areas, providing a reliable foundation for model training and evaluation.</p><p><strong>Result: </strong>The HR-Net achieves a mIoU and mPA of 85.50% and 91.23%, respectively. The YOLOv12 network attains a mAP and F1 score of 78.7% and 75.9%, respectively, for unsegmented images. The YOLOv12 model enhanced with Coordinate Attention (CAYOLOv12) is combined with the HR-Net model. Compared to the standalone YOLOv12 model, the HR-YOLO model improves mAP and F1 scores by 10% and 9.6%, respectively.</p><p><strong>Conclusions: </strong>The proposed approach provides an efficient and reliable technological solution for the intelligent recognition of key infrastructure in traffic safety monitoring systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}