Traffic Injury Prevention最新文献

{"title":"System failures and extreme behavior in fatal and injury crashes in South Australia","authors":"Lisa Wundersitz ,&nbsp;Simon Raftery","doi":"10.1080/15389588.2025.2454945","DOIUrl":"10.1080/15389588.2025.2454945","url":null,"abstract":"<div><h3>Objectives</h3><div>Within the road system, there are compliant road users who may make an error that leads to a crash, indicating a “system failure.” There are also road users who deliberately take risks and engage in dangerous or “extreme” behavior that leads to a crash. This study aims to assess the relative contribution of system failures and extreme behavior to guide the development of future strategies and interventions needed to create a safe system and prevent road trauma.</div></div><div><h3>Methods</h3><div>This study used the same methodology as Wundersitz et al. to provide an update on the relative contribution of system failures and extreme behaviors in more recent crashes. Two samples were used for the study: 157 fatal crashes from Coroner’s investigation files and 235 injury crashes from in-depth crash investigations conducted by the Center for Automotive Safety Research.</div></div><div><h3>Results</h3><div>Consistent with previous findings, the results indicated that the majority of fatal (70%) and injury crashes (93%) in South Australia were attributable to failures within the road transport system. In almost half of the fatal crashes and 72% of injury crashes, road users were fully compliant (i.e., no illegal behaviors). A comparison of the relative contributions over time revealed that the proportion of extreme behaviors in fatal crashes has decreased, which may be, at least partly, attributable to a reduction in alcohol-related crashes within South Australia.</div></div><div><h3>Conclusions</h3><div>Overall, the findings suggest that strategies continuing to focus on system wide improvements to the road transport system such as providing safe road infrastructure (e.g., side and center barriers) and the accelerated uptake of safe vehicle technologies (e.g., lane keeping technology, autonomous emergency braking) can be expected to be effective in reducing the incidence and severity of a large proportion of fatal and serious injury crashes. For more extreme behaviors, greater control of road user behavior may be required through the increased use of vehicle technologies and more holistic social health initiatives.</div></div>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":"26 7","pages":"Pages 785-793"},"PeriodicalIF":1.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473251","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":"Drug use among individuals injured in non-fatal motor vehicle crashes and related policies in 2023","authors":"Nae Y. Won ,&nbsp;Kelly K. Gurka ,&nbsp;Catherine W. Striley ,&nbsp;Sara Jo Nixon ,&nbsp;Linda B. Cottler","doi":"10.1080/15389588.2025.2456952","DOIUrl":"10.1080/15389588.2025.2456952","url":null,"abstract":"<div><h3>Objective</h3><div>Although driving-related policies aimed at mitigating drug-related motor vehicle crashes (MVCs) have been implemented in diverse communities, data regarding their effectiveness are largely absent. A comprehensive evaluation of these policies is necessary. Furthermore, as US states legalize the recreational use of cannabis, the impact of these policies on drug-related crashes also needs to be evaluated. The objective was to assess the association between drug use prevalence among individuals (age 16+) injured in non-fatal MVCs in 2023 and various related policies, including drug-impaired driving policies, sobriety checkpoints, enforcement programs, and state cannabis legalization status.</div></div><div><h3>Methods</h3><div>We analyzed 2023 emergency medical services (EMS) records of individuals (age 16+) injured in non-fatal MVCs across 19  US states where EMS personnel indicated drug use, excluding duplicate, incomplete, and alcohol-only records. Using these counts, we calculated the prevalence of drug use among individuals (age 16+) injured in non-fatal MVCs in each state. The association between drug use prevalence and state-level policies, including drug-impaired driving laws (i.e., per se or zero tolerance), sobriety checkpoints, enforcement programs, and cannabis legalization laws, was evaluated using adjusted Poisson regression with random effects for state differences. Policies were assessed individually and in a full model to evaluate their individual and additive effects.</div></div><div><h3>Results</h3><div>In 2023, 11,538 individuals (68.2% male) were injured in drug-related non-fatal crashes. Neither drug-impaired driving policies, the use of sobriety checkpoints, nor the implementation of State Judicial Outreach Liaisons influenced the prevalence of drug use among individuals injured in non-fatal crashes. In contrast, relative to states with no policy or cannabidiol/low tetrahydrocannabinol, those permitting recreational cannabis had significantly higher prevalence (adjusted prevalence ratio [aPR]: 1.57, 95% confidence interval [CI]: 1.22, 2.02). The implementation of sobriety checkpoints was associated with higher drug use prevalence (aPR: 1.59, 95% CI: 1.22, 2.09) when drug-impaired driving policies were absent, particularly in states permitting recreational cannabis.</div></div><div><h3>Conclusion</h3><div>Our findings show differences in drug use prevalence among individuals (age 16+) injured in non-fatal MVCs based on state-level policies, highlighting the need for holistic enforcement strategies to address drug-related crashes, especially amid the increasing risks associated with the legalization of recreational cannabis use.</div></div>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":"26 7","pages":"Pages 760-768"},"PeriodicalIF":1.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494830","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":"Real-world pedestrian crash reconstructions: Vehicle model validation and biomechanical injury analysis.","authors":"Luis Poveda, Logan E Miller, William Armstrong, Kevin Check, Fang-Chi Hsu, F Scott Gayzik, Ashley A Weaver, Joel D Stitzel, Karan Devane","doi":"10.1080/15389588.2025.2551888","DOIUrl":"https://doi.org/10.1080/15389588.2025.2551888","url":null,"abstract":"<p><strong>Objectives: </strong>The overarching objective of this study was to reconstruct five real-world pedestrian crashes using data from the Vulnerable Road User In-Depth Crash Investigation Study (VICIS) database, the Global Human Body Models Consortium (GHBMC) simplified pedestrian models, and morphed generic vehicle (GV) models reflecting U.S. vehicle front-end geometry to investigate pedestrian injury risks, compare simulated injury outcomes and contact kinematics with real-world observations, and evaluate the suitability of these simplified models for crash reconstruction.</p><p><strong>Methods: </strong>Five real-world pedestrian crashes from VICIS were reconstructed based on injury distribution and test data availability. Cases included four males (ages 14, 48, 56, and 64) and one female (age 57). Vehicles included three sport utility vehicles (SUVs) and two sedans, impacting at an average speed of 47 kph (range: 16-65 kph). Sedan and SUV GVs were morphed using computer-aided design (CAD) models to match front-end geometry. The windshield was modeled as a three-layer structure with fracture-enabled outer glass layers. Morphed models were validated against Euro New Car Assessment Program (NCAP) headform, upper legform, and lower legform tests using correlation and analysis (CORA) ratings. The models were used to reconstruct crashes by applying initial velocity and scaling GHBMC pedestrian models to match the case pedestrian height and weight. The contact points from simulations were compared with real-world crash evidence. AIS2+ injuries from the cases were compared to reconstructed results using injury metrics and risk functions.</p><p><strong>Results: </strong>The average ± <i>SD</i> CORA score for all pedestrian NCAP validation tests was 0.72 ± 0.1, indicating a good rating. Contact points from reconstructions closely matched real-world crashes. Brain injury criterion (BrIC) and cumulative strain damage measure (CSDM) injury risks (>90%) predicted cerebral injuries, while the Head Injury Criterion (HIC) injury risks remained low in two cases (<5%), underpredicting skull fractures. Chest deflection predicted thorax injury (injury risk >73%), whereas thoracic trauma index (TTI) risks were low (<50%). Tibia fractures from the cases were confirmed by injury risk estimations (>90%) using the revised tibia index (RTI).</p><p><strong>Conclusions: </strong>The GV-based pedestrian crash reconstruction framework demonstrated strong potential for real-world crash studies. CAD-based morphing enabled close matching of case vehicle front geometry, and material/structural tuning enhanced model responses aligned with physical vehicle data. The results of the reconstruction matched well with the actual crash data.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-12"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208352","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":"Grounded theory-based analysis of factors influencing driving behavior.","authors":"Zhi-Fang Wang, Yong-Qing Guo, Fu-Lu Wei, Dong Guo, Qing-Yin Li, Jahongir Pirov","doi":"10.1080/15389588.2025.2549888","DOIUrl":"https://doi.org/10.1080/15389588.2025.2549888","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to systematically explore the factors influencing driving behavior by analyzing the dynamic interactions among individual characteristics, external environmental conditions, and social influences, ultimately uncovering the complex relationships and coupling mechanisms behind these factors to support the development of intelligent driving systems and the optimization of traffic policies.</p><p><strong>Methods: </strong>The research employed grounded theory as a qualitative analytical approach, combining in-depth interviews and simulated driving experiments with 28 participants. A three-level coding process integrated with the SOR (Stimulus-Organism-Response) framework was utilized to dissect the interplay between drivers' internal states, environmental stimuli, and behavioral responses.</p><p><strong>Results: </strong>The findings revealed that driving experience, physiological conditions, and safety awareness directly shape decision-making processes, while environmental factors such as complex traffic scenarios and adverse weather conditions dynamically prompt drivers to adjust their strategies. Social norms were observed to exert indirect behavioral effects through situational interactions, and a significant positive correlation emerged between individual factors and environmental stimuli. Notably, the study highlighted complex relationships between drivers' experiential knowledge and their adaptability to real-time scenarios.</p><p><strong>Conclusions: </strong>This research underscores the complexity of driving behavior as a product of dynamically coupled individual, environmental, and social factors. By emphasizing the interdependence of human experience and situational adaptation, the outcomes provide a theoretical foundation for designing human-centric intelligent driving technologies and formulating traffic management policies that account for multidimensional behavioral influences.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126503","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":"Assessing drivers' psychophysiological states using heart rate, electrodermal activity, and pupillometry in real-world driving.","authors":"Gaetano Bosurgi, Orazio Pellegrino, Giuseppe Sollazzo, Alessia Ruggeri","doi":"10.1080/15389588.2025.2546651","DOIUrl":"https://doi.org/10.1080/15389588.2025.2546651","url":null,"abstract":"<p><strong>Objectives: </strong>The increasing availability of wearable, low-cost physiological sensors offers new opportunities for monitoring drivers' internal states during real-world driving, complementing traditional performance-based evaluations. This study investigates the potential of three biometric variables-heart rate, electrodermal activity, and pupil diameter-to identify psychophysiological conditions such as stress, workload, and fatigue in real-world road scenarios.</p><p><strong>Methods: </strong>Data were collected from 10 drivers on a 4.- km rural road segment featuring 14 selected curves. Physiological signals were analyzed using Fuzzy C-Means clustering to detect recurring latent states.</p><p><strong>Results: </strong>The analysis revealed weak correlations among the three indicators, suggesting they provide complementary information. Although heart rate did not show a consistent trend, dermal conductivity and pupil diameter exhibited cumulative responses, supporting their use as indicators of driver psychophysiological states. The following clustering technique identified two distinct psychophysiological profiles, varying along the road section.</p><p><strong>Conclusions: </strong>The findings highlight the usefulness of multimodal physiological data for identifying potentially demanding road segments and suggest implications for infrastructure design and real-time driver assistance systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126549","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":"Unsafe driving behaviours in northwest Iran: A cross-sectional study using observational methods.","authors":"Fatemeh Bakhtari Aghdam, David C Schwebel, Ali Jafari-Khounigh, Behjat Shokrvash, Sepideh Harzand-Jadidi, Homayoun Sadeghi-Bazargani, Leila Jahangiry, Shahab Papi, Kavous Shahsavari Nia","doi":"10.1080/15389588.2025.2551154","DOIUrl":"https://doi.org/10.1080/15389588.2025.2551154","url":null,"abstract":"<p><strong>Objective: </strong>This cross-sectional study using observational methods study was conducted in 2022 to investigate risky driving behaviors among 3005 drivers in various areas of Tabriz, the largest city in northwest Iran. Observations were made when drivers stopped at intersections or before entering their government workplace.</p><p><strong>Methods: </strong>Observational sites represented low, middle, and high income areas, and locations serving local areas, commuting areas, and workplaces. Observations occurred at various times of day and were conducted by recording drivers' behavior using a checklist based on the Martinez-Sanchez method. Chi-square and binary logistic regression analyses examined relations between demographic variables and drivers' behavior.</p><p><strong>Results: </strong>Among the observed drivers, 67.39% failed to use seat belts, 29.72% used mobile phones while driving, and 74.24% stopped beyond the stop line. Women used seat belts 1.64 times more often than men [OR = 1.64; 95% CI: 1.36-1.97]. Drivers estimated to be under 25 years and aged 25-40 years used mobile phones significantly more often than drivers estimated to be over age 50 [OR = 2.65; 95% CI: 1.96-3.60], [OR = 1.75; 95% CI: 1.34-2.30]. Drivers were significantly more likely to use mobile phones on weekends than during the week [OR =1.49; 95% CI: 1.15-1.93] and at noon compared to the morning [OR = 1.25; 95% CI: 1.03-1.53]. Drivers observed in middle socioeconomic status (SES) locations failed to fasten seat belts 1.23 times more frequently than drivers in high SES areas [OR = 1.23; 95% CI: 1.01-1.51]. Drivers at workplaces and in local areas failed to fasten their seat belts 2.07 and 1.78 times more than drivers in commuting areas, respectively [OR = 2.07; 95% CI: 1.71-2.49; OR = 1.78; 95% CI: 1.45-2.17].</p><p><strong>Conclusion: </strong>In summary, we observed considerable risk-taking behavior among drivers in Tabriz, Iran, with the highest risk occurring among male and younger drivers. Multifaceted intervention programs and policymaking, building off successful programs in other countries, should be implemented to increase safe driving behaviors.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126493","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":"Impact of Music Tempo on Driving Behavior and Vigilance in Speed-Limited Areas on Urban Roads.","authors":"Yihe Huo, Zhipeng Peng, Hengyan Pan, Duo Li, Yonggang Wang","doi":"10.1080/15389588.2025.2552351","DOIUrl":"https://doi.org/10.1080/15389588.2025.2552351","url":null,"abstract":"<p><strong>Objective: </strong>The study aims to investigate the effects of varying music tempos on driving behavior and vigilance across three distinct urban speed-limited environments: generic roads, school zones, and work zones, utilizing a driving simulation experiment.</p><p><strong>Methods: </strong>The study constructed three urban road scenarios with distinct speed limit challenges: generic road section with 60 km/h limit, school zone with 30 km/h limit, and work zone with 30 km/h limit and a lane closure. Participants were recruited to conduct driving simulation experiments, executing speed-limited tasks under conditions of no music, slow tempo music, and fast tempo music. Throughout the process, their changes in driving behaviors and vigilance were recorded. Using non-parametric tests, the study examined differences in speed fluctuation, mean deceleration and skin conductance level (SCL) across varying music tempos, as well as the differential effects of music tempo on drivers' handling of distinct speed-limited tasks.</p><p><strong>Results: </strong>The results revealed that playing music in the easier speed-limited task scenario (generic road section) reduced speed fluctuations and enhanced vigilance. In the school zone with a 30 km/h limit, fast tempo music notably reduced both speed fluctuation and mean deceleration while experiencing lower SCL. In the work zone scenario, where the speed-limited task was more challenging, listening to slow tempo music appeared to distract drivers, leading to increased speed fluctuations and deceleration. In contrast, the absence of music was associated with heightened vigilance during this task. Ultimately, while the introduction of music can alleviate physiological stress induced by challenging driving tasks, it may also impair driving behavior.</p><p><strong>Conclusions: </strong>The findings provide important insights for intelligent driving systems to select appropriate music types based on different driving tasks to enhance driving experience and safety.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126498","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":"Examining traffic violations in severe casualty truck crashes: A text mining and reliable network analysis of narrative reports.","authors":"Yunfei Zhao, Kai Kang, Wenjian Jia, Zhe Guo, Jie Zhang, Tong Zhu","doi":"10.1080/15389588.2025.2553194","DOIUrl":"https://doi.org/10.1080/15389588.2025.2553194","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Trucks are more likely to be involved in severe casualty crashes compared with other vehicle types. The elimination of traffic violations is crucial to preventing severe casualty truck crashes. However, there is a lack of comprehensive analyses of truck violations and their conditions related to severe casualty crashes. This study aims to identify thematic communities of truck driver violations through a modeling framework integrating text mining and reliable network analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study collected 432 textual reports of severe truck casualty crashes in China from 2013 to 2020, which were divided into crash narratives and metadata for separate preprocessing. For the narrative part, the ELECTRA model was used for Chinese word segmentation and part-of-speech tagging, and keywords were extracted by combining with TF-IDF. The metadata was processed through named entity recognition, geocoding, etc., and then merged with the narrative keywords. Association rules were mined by the Apriori algorithm to construct a network with keywords as nodes and lift values as edge weights, which was visualized by the ForceAtlas2 algorithm. The Leiden algorithm was adopted to detect thematic communities, whose significance was validated by QStest.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Text mining results reveal 77 most relevant keywords extracted from 432 police narratives. Overloading and speeding emerge as predominant traffic violations, correlating with 43% and 30% of severe casualty truck crashes, respectively. A total of four overloading and five speeding statistically significant thematic communities are identified. Notably, the circumstances associated with truck overloading and speeding manifest distinct characteristics. For overloading, conditions contributing to severe casualty crashes encompass rural highways with curves or slopes, provincial or national highways in the afternoon, expressways during nighttime, and locations proximate to signalized intersections. In contrast, five circumstances are linked to speeding: curved or sloped road segments during the afternoon, rural highways in autumn, straight road sections during the night, work zone areas on four-lane roadways, and un-signalized intersections on weekdays. Moreover, we also extracted vehicle and driver features across diverse environments, facilitating the identification of key elements for preventing severe casualty truck crashes. For instance, light trucks exhibit a higher susceptibility to severe casualty crashes attributed to overloading on rural highways.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates the advantages of textual data and reliable network analysis. Text data analysis proves to be more convenient, yielding a richer array of comprehensive information while demanding less subjective judgment. The findings of this paper inform consequent enforcement and engineering measures for mitigating severe casualty truck cra","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126515","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":"Developing an XGBoost based model to predict the probability of truck crashes driven by macro operation and insurance data.","authors":"Yiping Wu, Hongpeng Zhang, Peng Song, Xiaoheng Sun, Ji Meng, Jun Ma, Liwei Gao","doi":"10.1080/15389588.2025.2545002","DOIUrl":"https://doi.org/10.1080/15389588.2025.2545002","url":null,"abstract":"<p><strong>Objective: </strong>Truck accidents caused significant casualties usually. Establishing a scientific truck accident prediction model and identifying the primary causes are crucial for proactive accident prevention.</p><p><strong>Methods: </strong>The proposed model was developed using annual operational behavior data and corresponding insurance claim information from commercial trucks. Prior to model training, multicollinearity among predictor variables was addressed to ensure model interpretability and stability. Model performance was evaluated using recall, F1 score, and overall prediction accuracy, including external validation with a temporally separated dataset from the same driver population. To reduce input data dependency, an input dimensionality reduction analysis was conducted to determine the minimal data requirements. SHAP (Shapley Additive Explanations) values and principal component coefficients were employed to extract the main factors influencing truck accidents.</p><p><strong>Results: </strong>The truck accident prediction model with a recall rate of 84.21% and an F1 score of 85.33%. The prediction accuracy of our developed model reached 87.59% when using new data from the same group of truckers in the subsequent year for validation. Additionally, the minimum data requirement set for our developed model was found to be the feature combination of load capacity, road segment type, and driving time, through analyzing the relationship between model prediction accuracy and feature inputs with different dimensions. Based on the suggested model inputs, the recall rate and F1 score of the prediction model are 86.84% and 84.62%, respectively. The main influencing factors analyzed by SHAP values and Principal Component Analysis (PCA) coefficients indicated that the trucker's familiarity with the road and the type of road segment significantly impact the probability of accident occurrence.</p><p><strong>Conclusions: </strong>This research innovatively establishes a macro data-driven truck accident prediction model alleviating the difficulty of data collection as well as guaranteeing the prediction accuracy.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071135","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":"Predicting head impact conditions in vehicle-to-pedestrian impacts through computational human modeling.","authors":"Jingwen Hu, Yang-Shen Lin, Kyle Boyle, Anne Bonifas, Chin-Hsu Lin, Whitney Tatem, Peter Martin","doi":"10.1080/15389588.2025.2547041","DOIUrl":"https://doi.org/10.1080/15389588.2025.2547041","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to use finite element (FE) pedestrian and vehicle models to generate a virtual database of pedestrian impacts and develop prediction models for pedestrian head impact conditions, which are important to evaluate the effects of vehicle front-end designs on pedestrian head injury responses.</p><p><strong>Methods: </strong>The generic vehicle (GV) models used in Euro NCAP originally developed based on European vehicles were morphed into 20 U.S. vehicle front-end geometries across a wide range of vehicle types and characteristics. A total of 240 FE pedestrian impact simulations were conducted using the 20 morphed GV models with four sizes of pedestrian human body models (6-year-old, small female, midsize male, and large male) at three impact speeds (30, 40, and 50 kph). A set of predictors, including vehicle front-end geometry, pedestrian size, vehicle impact speed, and pedestrian wrap around distance (WAD) were selected based on literature to predict head impact time (HIT), head contact velocity, and head contact angle. <i>R</i>² values and root-mean-square-error (RMSE) were used to evaluate the quality of the prediction models.</p><p><strong>Results: </strong>High correlations and good accuracies were achieved in the prediction models for HIT (<i>R</i>² = 0.979 and RMSE = 6.61 ms), head impact velocity (<i>R</i>² = 0.799 and RMSE = 1.39 m/s), and head impact angle (<i>R</i>² = 0.846 and RMSE = 7.95 deg for adult pedestrians). It was found that impact speed, WAD, hood angle, and hood height are statistically significant variables for predicting the pedestrian head impact conditions. HIT is highly predictable in pedestrian impacts, while the head impact velocity and head impact angle are associated with larger variations in the selected impact conditions. This indicates a potential need of varying impact velocity and angle for future vehicle evaluations of pedestrian head protection.</p><p><strong>Conclusions: </strong>This study generated a virtual database of pedestrian impacts with a wide range of vehicle front-end geometries, and developed prediction models to use vehicle front-end geometry, pedestrian size, impact speed, and WAD to predict pedestrian HIT, head contact velocity, and head contact angle.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071159","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}