{"id":2567,"date":"2026-10-06T13:30:57","date_gmt":"2026-10-06T11:30:57","guid":{"rendered":"https:\/\/www.vufo.de\/unkategorisiert\/transportation-research-interdisciplinary-perspectives\/"},"modified":"2026-10-06T13:46:45","modified_gmt":"2026-10-06T11:46:45","slug":"transportation-research-interdisciplinary-perspectives","status":"publish","type":"post","link":"https:\/\/www.vufo.de\/en\/publications\/transportation-research-interdisciplinary-perspectives\/","title":{"rendered":"Transportation Research Interdisciplinary Perspectives"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Predicting specific injury patterns from crash data: a GIDAS-based and manufacturer-independent logistic regression approach for next generation eCall<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\"><strong>Background:<\/strong> While prehospital traffic fatalities remain a critical challenge, current eCall systems transmit limited injury-relevant data, and existing prediction models often fail to support targeted prehospital triage. This study developed and validated a manufacturer-independent, data-driven framework to predict tactically relevant, region-specific injury patterns to enhance early rescue chain activation. <\/p>\n\n<p class=\"wp-block-paragraph\"><br\/><strong>Methods:<\/strong> Logistic regression models were developed using GIDAS data for front-row occupants, stratified by impact direction. Clinically relevant injury categories were defined based on prehospital tactical criteria. Pre dictors included crash severity (Energy Equivalent Speed, EES), restraint use, and vehicle characteristics. Per permance was assessed via cross-validation and external validation on a recent GIDAS subset, utilizing metrics robust to class imbalance.   <\/p>\n\n<p class=\"wp-block-paragraph\"><br\/><strong>Results:<\/strong> Across all injury groups, the models demonstrated high discrimination (AROC &gt; 0.85 for most groups), robust calibration, and biomechanically plausible associations. Energy-equivalent speed (EES) consistently emerged as the strongest and most reliable predictor of injury severity. Optimized classification thresholds supported the prediction of both global and region-specific trauma. External validation confirmed reproducibility for thorax, head, and overall severe injuries, maintaining high sensitivity and specificity under optimized thresholds.   <\/p>\n\n<p class=\"wp-block-paragraph\"><br\/><strong>Conclusion: <\/strong>Ultimately, this study provides a scientifically robust framework for predicting region-specific injury patterns, with results that can be translated into next-generation eCall systems to inform prehospital triage and decision-support. Clinically, this predictive framework translates crash data into triage intelligence, enabling emergency dispatchers to allocate specialized trauma resources more precisely. While the models demonstrate strong predictive performance on the GIDAS dataset, prospective multicenter validation is ongoing to assess their real-world operational utility.  <\/p>\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predicting specific injury patterns from crash data: a GIDAS-based and manufacturer-independent logistic regression approach for next generation eCall Abstract Background: While prehospital traffic fatalities remain a critical challenge, current eCall systems transmit limited injury-relevant data, and existing prediction models often fail to support targeted prehospital triage. This study developed and validated a manufacturer-independent, data-driven framework [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34,32],"tags":[],"class_list":["post-2567","post","type-post","status-publish","format-standard","hentry","category-english","category-publications"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Transportation Research Interdisciplinary Perspectives<\/title>\n<meta name=\"description\" content=\"Data-driven injury prediction for road traffic accidents: A new approach to improving eCall systems and pre-hospital triage.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.vufo.de\/en\/publications\/transportation-research-interdisciplinary-perspectives\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Transportation Research Interdisciplinary Perspectives\" \/>\n<meta property=\"og:description\" content=\"Data-driven injury prediction for road traffic accidents: A new approach to improving eCall systems and pre-hospital triage.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.vufo.de\/en\/publications\/transportation-research-interdisciplinary-perspectives\/\" \/>\n<meta property=\"og:site_name\" content=\"VUFO Dresden\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-06T11:30:57+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-06T11:46:45+00:00\" \/>\n<meta name=\"author\" content=\"Katja Bathow-Wels\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" 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