Traffic Injury Prevention最新文献

{"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}

{"title":"Factors associated with child safety seat use in Bangkok Metropolitan Region, Thailand.","authors":"Araya Chinaphan, Ratchaneewan Sinitkul, Cholatid Ratanatharathorn, Nicha Taschanchai","doi":"10.1080/15389588.2025.2559121","DOIUrl":"https://doi.org/10.1080/15389588.2025.2559121","url":null,"abstract":"<p><strong>Objectives: </strong>To understand factors associated with proper child safety seat (CSS) use in the Bangkok Metropolitan Region (BMR), Thailand, after the child restraint legislation and to provide evidence to inform policy for increasing proper CSS use.</p><p><strong>Methods: </strong>A cross-sectional study was conducted. Primary caregivers of at least one child aged 0-6 years or height 135 cm or less, who own a car, reside in BMR, and achieve literacy in the Thai language were included. The recruitment was done by distributing posters with QR codes to access information sheets and online self-administered questionnaires both online and offline (Pediatrics outpatient and postpartum units at Ramathibodi Hospital; 273 public early childhood centers; and 102 kindergartens in Bangkok) between April and December 2024. The questionnaire comprised questions regarding demographic data, knowledge, attitude, practice, and other information about CSS. Data were compared between the proper and improper CSS users <i>via</i> Stata version 17.</p><p><strong>Results: </strong>330 respondents with a median (Q1, Q3) age of 38 (34, 41) years were included; most were female (83.2%) and residents of Bangkok (68.7%). Two hundred ninety-six respondents (89.7%) reported CSS usage, with 170 respondents reporting regular use (51.5%). Among respondents, 135 (40.9%) were categorized as proper users (regularly use an age-appropriate type of CSS and locate it on the back seat). Logistic regression showed factors associated with proper CSS used were higher household income (OR 9.97, 95%CI: [4.06-24.52], <i>p</i> < 0.001) and barriers to using CSS including child-related barriers (OR 0.10, 95%CI: [0.05-0.22], <i>p</i> < 0.001), caregivers-related barriers (OR 0.19, 95%CI: [0.04-0.99], <i>p</i> = 0.049), and car-related barrier (OR 0.38, 95%CI: [0.15-0.92], <i>p</i> = 0.031). The leading reported barriers to CSS use were children's refusal (42%) and the high cost of CSS (23%). The trusted sources of information regarding CSS were social media/internet (43%) and healthcare providers (34%). Respondents preferred educational intervention for improving knowledge (22%) and tax deduction policy (17%) to help increase CSS use.</p><p><strong>Conclusions: </strong>After the child restraint legislation, the proper usage of CSS to ensure child safety was still low. Factors associated with proper CSS usage included higher household income and barriers to using CSS. Multilevel interventions and policies were suggested to address the issues. Further research could be done to evaluate the effectiveness of those measures and their impacts on increasing proper CSS usage in Thailand.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281786","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":"Expressway conflict risk mechanism considering the interactions between vehicle-group and road-segment.","authors":"Ling Wang, Jiajin He, Yunfan Zhang, Yingying Xing, Juneyoung Park, Wanjing Ma","doi":"10.1080/15389588.2025.2558120","DOIUrl":"https://doi.org/10.1080/15389588.2025.2558120","url":null,"abstract":"<p><strong>Objectives: </strong>There have been numerous studies on conflict risk for expressways, with the majority of previous studies focusing on road-segments' conflict risk while neglecting the impact of moving vehicles. In recent years, some studies have begun to work on vehicle-group without the consideration of the traffic on the road-segment. However, as the vehicle-group travels along the road-segment, the conflict risk of road-segment and vehicle-group will interact with each other. The aim of this study is to analyze the interactive mechanism of conflict risk between vehicle-groups and road-segments on expressways.</p><p><strong>Methods: </strong>This study utilized high-resolution vehicle trajectory data to separately build conflict risk prediction models for vehicle-groups and road-segments. The best performing models were selected and explainability algorithms were applied. The analysis then focused on two aspects: (1) the influence of downstream high-risk vehicle-groups on upstream road-segment conflict risk and (2) the impact of geometric and traffic parameters of downstream road-segments on the conflict risk of vehicle-groups.</p><p><strong>Results: </strong>The results show that vehicle-group characteristics significantly affect road-segment conflict risk. When high-risk vehicle-groups appear downstream, the conflict risk of the road-segment increases by about 6%, and the impact is stronger when the propagation distance is shorter. In turn, when the conflict risk of a downstream road-segment increases, this risk propagates upstream through the traffic flow, affecting the behavior of vehicle-groups and raising their conflict risk. A difference of 29% in vehicle-group conflict risk was observed depending on the median downstream road-segment conflict risk.</p><p><strong>Conclusions: </strong>This study demonstrates that vehicle-groups and road-segments interact in the propagation of expressway conflict risk. Integrating these two dimensions enables more accurate conflict risk prediction and analysis. This research provides a novel perspective by integrating vehicle-group and road-segment interactions for more accurate conflict risk prediction and analysis.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281726","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":"Simulation-based assessment of driving confidence in hazardous situations under different warning levels.","authors":"Erlong Lou, Haijian Li, Guoqiang Zhao, Lingqiao Qin, Xiaohua Zhao","doi":"10.1080/15389588.2025.2559128","DOIUrl":"https://doi.org/10.1080/15389588.2025.2559128","url":null,"abstract":"<p><strong>Objective: </strong>Although warning systems in connected environments have become increasingly common, their psychological impact on driving confidence remains underexplored. This study aims to analyze driving confidence under hazardous road events-such as emergency braking of front vehicles (EB-FV), work zones (WZ), and tunnels (Tun)-in response to warning systems, using a connected simulation platform.</p><p><strong>Methods: </strong>By integrating traffic psychology using hazardous event warnings with connected-vehicle technology, a unique perspective that has not been covered in previous studies is provided. Driving confidence was quantified using driving simulation technology in two dimensions: speed performance and driving operations.</p><p><strong>Results: </strong>The results show that predictive warning systems significantly improve driver confidence and control. Specifically, in the Tun, compared to the no-warning condition, the average driving speed decreased by 16.38%, and speed variability StdV decreased by 27.75%. Additionally, steering control was more stable, with a 18.40% decrease in steering wheel angle variability (SDSA) in the EB-FV scenario, and 7.31% in the WZ scenario.</p><p><strong>Conclusions: </strong>Additionally, the study highlights a significant improvement in driver confidence when warning information is provided. The conclusions are particularly applicable to structured road environments with reliable V2X communication and assume that drivers have some degree of familiarity with connected systems. This study provides theoretical and practical insights into the design of adaptive warning strategies for future intelligent transportation systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281766","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":"Incidence and outcomes of road traffic crashes among commercial motor tricycle drivers in Kumasi, Ghana: A population-based survey.","authors":"Adam Gyedu, Mohammed Abdullah, Sakinah Sulaiman, Peter Donkor, Charles Mock","doi":"10.1080/15389588.2025.2557504","DOIUrl":"https://doi.org/10.1080/15389588.2025.2557504","url":null,"abstract":"<p><strong>Objectives: </strong>The injury burden of motorized tricycles in African countries is not well-known despite their increasing use for commercial activities on the continent. To address this gap, we sought to understand the injury burden and crash risk factors for commercial motor tricycles (CMT) in Ghana.</p><p><strong>Methods: </strong>We conducted a survey of all CMT drivers at 11 groupings within Kumasi, Ghana. The survey utilized a structured questionnaire based on previous injury questionnaires used extensively in Ghana. The questionnaire sought information about characteristics and modifiable risk factors for road traffic crashes (RTCs) as well as safety-related road signs. The primary outcome was respondents experiencing at least one RTC in the past 1 year. Chi-square tests were used to determine differences between the primary outcome and various covariates.</p><p><strong>Results: </strong>There were 84 RTCs reported by the 710 respondents over the past 1-year with an incidence of 11.8%. Half (48%) of crashes caused injuries. Drivers reported overloading of vehicles (32%), not having a valid license (26%), never wearing a helmet (92%), long work hours (median 10 [range: 3-18] hours/day), and lack of scheduled maintenance (52%). Drivers had low knowledge of road signs (e.g. only 41% could identify a \"give way\" sign). Consumption of the stimulant \"ataya\" was higher among drivers with crashes in the past year compared to those without (34% vs 16%, <i>p</i> < 0.001).</p><p><strong>Conclusions: </strong>There is a significant injury burden from CMTs in Ghana. Several risk factors should be addressed: vehicle overloading, low vehicle maintenance, prolonged work hours, low helmet use, low knowledge of safety-related signage, and use of the stimulant \"ataya.\"</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-7"},"PeriodicalIF":1.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281734","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":"Injury risk curves for motorcycles in the United States.","authors":"Paolo Terranova, Feng Guo, Zachary Doerzaph, Miguel A Perez","doi":"10.1080/15389588.2025.2556958","DOIUrl":"https://doi.org/10.1080/15389588.2025.2556958","url":null,"abstract":"<p><strong>Objective: </strong>This study introduces a comprehensive injury risk model for motorcycle collisions tailored to the United States (U.S.). The proposed innovative approach enables the prediction of injury risk across the full range of crash speed and vehicle orientations, making it an essential tool for evaluating emerging safety measures for motorcycles.</p><p><strong>Methods: </strong>Data from the Motorcycle Crash Causation Study (MCCS) is used to train two multivariate regression models for predicting motorcycle injury risk. The data is weighted to align with national crash statistics and effectively represents the U.S. crash population. The models adopt motorcycle impact speed and vehicle-relative speed as key predictors while incorporating rider age, helmet use, and opponent vehicle type as covariates. The motorcycle's principal direction of force (PDoF) is also employed as a surrogate metric for the vehicle orientation, enabling the models to account for the full range of two-vehicle crash configurations.</p><p><strong>Results: </strong>The analysis clearly indicates that the motorcycle front-end crash-i.e., the motorcycle's front collides with any side of the opposing vehicle-is the most dangerous crash configuration. Additionally, the results demonstrate significant variability in injury likelihood based on the PDoF, emphasizing the influence of vehicle alignment and orientation on riders' injury outcomes.</p><p><strong>Conclusions: </strong>This study provides the first comprehensive injury risk models for motorcycle-involved two-vehicle collisions in the U.S., offering critical insights into the role of speed and vehicle orientation in injury outcomes. While further validation with larger datasets is required, the findings serve as a foundation for the evaluation of the safety benefits of emerging traffic safety systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253748","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}