Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2026. National Academy of Sciences. All rights reserved. http://blogs.law.harvard.edu/tech/rss tris-trb@nas.edu (Bill McLeod) tris-trb@nas.edu (Bill McLeod) Transport Research International Documentation (TRID) https://trid.trb.org/Images/PageHeader-wTitle.jpg https://trid.trb.org/ An organized review of micromobility factors contributing to accidents, market and service trend, and related mishaps https://trid.trb.org/View/2672724 Tue, 10 Mar 2026 09:54:58 GMT https://trid.trb.org/View/2672724 Improvements aiming at a safer living environment by analyzing crash severity through the use of boosting-based ensemble learning techniques https://trid.trb.org/View/2672722 Tue, 10 Mar 2026 09:54:58 GMT https://trid.trb.org/View/2672722 A comparative analytical framework for modeling road fatalities with count regression techniques https://trid.trb.org/View/2652959 Fri, 06 Feb 2026 13:52:06 GMT https://trid.trb.org/View/2652959 Determinants of injury severity in traffic accidents. Evidence from a developing country https://trid.trb.org/View/2652558 Fri, 06 Feb 2026 13:52:06 GMT https://trid.trb.org/View/2652558 Uncovering latent structures of crash typology in narcotic-involved fatal crashes for safe system interventions https://trid.trb.org/View/2647545 Fri, 06 Feb 2026 13:52:06 GMT https://trid.trb.org/View/2647545 Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above https://trid.trb.org/View/2652421 Fri, 06 Feb 2026 08:45:31 GMT https://trid.trb.org/View/2652421 Beyond the norm: Identifying rare and high-risk pedestrian crash patterns using unsupervised learning https://trid.trb.org/View/2655974 Wed, 04 Feb 2026 17:05:19 GMT https://trid.trb.org/View/2655974 Segmentation of Risk Factors for Fatal Crashes at Urban Signalized Intersections: A Multi-Perspective Model Approach https://trid.trb.org/View/2659304 Fri, 30 Jan 2026 14:48:45 GMT https://trid.trb.org/View/2659304 Identification of Work Zone Crash Characteristics https://trid.trb.org/View/2635926 Mon, 26 Jan 2026 17:40:24 GMT https://trid.trb.org/View/2635926 Regression modelling in traffic crash reconstruction https://trid.trb.org/View/2642431 Thu, 15 Jan 2026 14:31:02 GMT https://trid.trb.org/View/2642431 Factors associated with alcohol-impaired driver crash deaths in the United States, 2018–2022 https://trid.trb.org/View/2643811 Thu, 15 Jan 2026 14:31:02 GMT https://trid.trb.org/View/2643811 Uncovering contextual risk patterns in cannabis-involved fatal crashes: A data-driven approach to public health-oriented road safety https://trid.trb.org/View/2643898 Thu, 15 Jan 2026 14:31:02 GMT https://trid.trb.org/View/2643898 Crash typology of professional cycling crashes https://trid.trb.org/View/2636598 50% of impacts). Notably, body-first head impacts dominated the crash profiles (92%), with the torso or arms contacting the ground before the head, while direct head-first impacts comprised 8 % of cases. Impact severity was distributed relatively evenly across low (30 %), medium (33%), and high (36%) categories, with collision-related crashes being more likely to result in high-severity outcomes than non-contact crashes. These findings reveal a potential mismatch between current helmet testing protocols and the predominant mechanisms observed in professional cycling crashes. Video-based analysis provides critical insights into impact mechanisms that are overlooked by traditional injury reporting methods, particularly highlighting the prevalence of body-first impacts and side-rim head impacts. This crash typology may provide a foundation for future biomechanical studies and could support the development of helmet testing methods that better represent real-world cycling impact scenarios.]]> Thu, 15 Jan 2026 14:31:02 GMT https://trid.trb.org/View/2636598 A comparative study of the spatial variability of cyclist, pedestrian, and motorised two-wheeler rider fatalities in an urban area https://trid.trb.org/View/2640738 Thu, 15 Jan 2026 14:31:01 GMT https://trid.trb.org/View/2640738 Predicting commercial motor vehicle crash severity in Kansas using explainable machine learning https://trid.trb.org/View/2632910 Fri, 09 Jan 2026 16:58:53 GMT https://trid.trb.org/View/2632910