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A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
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Injury severity prediction of cyclist crashes using random forests and random parameters logit models.

Antonella Scarano1, Maria Rella Riccardi1, Filomena Mauriello1

  • 1University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.

Accident; Analysis and Prevention
|September 8, 2023
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Summary

Cyclist safety is improved by understanding crash factors. Key predictors for fatal crashes include vehicle maneuvers and driver gender, while for serious injuries, the bike leaving the road is critical.

Keywords:
Active travelCrash contributory factorsCyclist safetyEconometric modelsMachine learningSafety countermeasures

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Area of Science:

  • Road Safety Engineering
  • Transportation Science
  • Data Science in Accident Analysis

Background:

  • Cyclists face disproportionately high road traffic injury and fatality rates.
  • Understanding crash factors is crucial for developing effective safety measures.
  • Current knowledge gaps limit context-specific interventions for cyclist safety.

Purpose of the Study:

  • To investigate factors influencing cyclist crash severity.
  • To identify key road, environmental, vehicle, driver, and cyclist characteristics.
  • To compare machine learning and econometric modeling for crash analysis.

Main Methods:

  • Analysis of 72,363 cyclist crashes in Great Britain (2016-2018).
  • Implementation of Random Forest (RF) algorithms, including Random Survival Forest (RSF).
  • Application of Random Parameters Logit Model (RPLM) to assess unobserved heterogeneity.

Main Results:

  • RSF identified critical predictors for fatal crashes (e.g., vehicle maneuver, driver gender) and serious injuries (e.g., bike leaving carriageway).
  • Generated 361 if-then rules for fatal and 349 for serious injury crashes.
  • RPLM revealed significant factors like cyclist age ≥ 75, male cyclist gender, and driver age 55-64 impacting severity.

Conclusions:

  • RF and RPLM provide complementary insights into cyclist crash severity factors.
  • Identified factors can inform targeted safety countermeasures.
  • Recommendations aim to enhance cycling safety and promote cycling as a transport mode.