Print Email Facebook Twitter A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes Title A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes Author Alnawmasi, Nawaf (University of Hail) Alogaili, Asim (Majmaah University) Rangaswamy, Rakesh (Transportation Engineer) Oviedo-Trespalacios, O. (TU Delft Safety and Security Science) Date 2024 Abstract This study estimates mixed logit models taking into account heterogeneity in means and partially constrained parameters in order to explore possible shifts within parameters over time to study factors influencing bicyclist injury severity outcomes. Separate statistical models are estimated for two bicyclist helmet-wearing scenarios (helmet and non-helmet) using a comprehensive dataset from Florida covering a three-year period to assess COVID-19 effects from the 1st of January 2019 to the 31st of December 2021. This research evaluates several factors influencing helmeted and non-helmeted bicyclist injury severity, encompassing the attributes of drivers and cyclists, the environment and weather, the features of the roads and their temporal aspects, and the different types of vehicles. The performed analysis further enhances model robustness by assessing the temporal stability and transferability across different contexts through likelihood ratio tests, alongside an in-depth examination of the temporal consistency of explanatory variables via marginal effects analysis, confirming significant variations between non-helmeted and helmeted bicyclist models and revealing temporal shifts in factors affecting injury severity during the study period. Findings from the model estimations identify several significant variables with consistent parameter estimates across years. Stop signs, cycling with traffic, and dark, unlit conditions increase severe injury risk in non-helmet models, while the stop sign indicator consistently reduces severe injury risk in helmet models. Statistically significant random parameters are identified across different years and helmet-wearing scenarios, including the male driver indicator, which exhibits varying effects on injury severity. Out-of-sample prediction analysis suggests helmets reduce severe injury probability but may increase minor injuries and decrease no-injury accidents, indicating potential risk compensation behavior among helmeted bicyclists. Although helmets offer protection against severe injuries for bicyclists, it is crucial to adopt a comprehensive safety approach, particularly given the evolving demographics of bicyclists amid the COVID-19 outbreak. This entails considering factors like bicyclist and driver behavior, environmental conditions, and infrastructure enhancements. Policymakers, road safety professionals, and advocacy groups should collaborate to develop holistic strategies to address the determinants of bicycle crash severity outcomes and enhance safety measures for bicyclists across diverse road environments. Subject BicyclistsHelmet wearingHeterogeneity in the meansInjury severityPartially constrained modelRandom parameters logitTemporal stability To reference this document use: http://resolver.tudelft.nl/uuid:c41d5c92-8c65-4fe1-9419-ed0af77ff56d DOI https://doi.org/10.1016/j.amar.2024.100338 Embargo date 2024-11-30 ISSN 2213-6657 Source Analytic Methods in Accident Research, 43 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 Nawaf Alnawmasi, Asim Alogaili, Rakesh Rangaswamy, O. Oviedo-Trespalacios Files file embargo until 2024-11-30