Predictive modelling of injury severity in bicycle–motor vehicle collisions utilizing learning vector quantization: a case study of Britain’s cycling capital

Cambridge is truly known as Britain’s cycling capital but this has all been achieved without any real cycling infrastructure. Therefore, safety concern is the persistent barrier to cycling in Cambridge and its reputation is on the decline. Thus in response to the small number of the literatures on prediction models for cyclist related injuries, this study presents a modelling technique that applies learning vector quantization network to predict injury severity of cyclist. By discovering the potentially relationships between the injury levels and the factors that contribute to their generation, the model predicts the likelihood of the injury into two classes: slight and killed/seriously injured. The findings display that the effect of junction actions are almost double. Following this, T/staggered junction on an unclassified bend was discovered as UK's cycling capital’s “collision hotspots”. Subsequently, absence of a sufficient number of crossing facilities for cyclists had the largest effect. All other things being held near equal; rush hours during the weekdays, vehicle manoeuvre due to a poor turn and parked vehicle, going ahead bend manoeuvre by cyclist, limited modern protected bike lanes, wet road surface owing to inclement weather, vehicle blind spot and driving too close, visual distraction caused by adverse lighting condition and dazzle of sun, and junction control commonly at situations of give-way or uncontrolled intersections. The study then ends by maximising the overall predictive accuracy through contacting the most sensitive predictors into the model. Consequently, a professional road safety education needs to be delivered so as to crack down the human failure as a main contributory factor.

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    • © 2019 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Siamidoudaran, Meisam
    • Siamidodaran, Mehdi
    • Iscioglu, Ersun
  • Publication Date: 2021-3

Language

  • English

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  • Accession Number: 01766469
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 26 2021 3:01PM