Highlights A high incidence of bicycle crashes on Dublin tram tracks was observed in this study. Unsuccessful crossings were common at locations with kerbs or traffic pressures. Modeling shows that the crossing angle is a strong predictor of crossing success. Interventions: track realignments, jughandle lanes, and traffic reduction. This study demonstrates how video analyses help us understand bicycle crash causes.

    Abstract Introduction: Single Bicycle Crashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis. Method: This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles. Results: Modeling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage. Conclusions: Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more – ideally 90°. Practical Applications: The SMoS framework and the open-source SafeCross1 https://github.com/KevGildea/SafeCross/. application offer actionable insights and tools for enhancing cyclist safety around tram tracks.


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    Titel :

    Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success


    Beteiligte:
    Gildea, Kevin (Autor:in) / Hall, Daniel (Autor:in) / Mercadal-Baudart, Clara (Autor:in) / Caulfield, Brian (Autor:in) / Simms, Ciaran (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-09-20


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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