Highlights Zonal collision prediction models that explicitly incorporate bike network indicators are developed. Several network indicators were developed using Graph theory and their effect on safety was investigated. The models included actual cyclist exposure (bike kilometers travelled) as opposed to relying on exposure proxies. Generalized linear regression and full Bayesian techniques with spatial effects were adopted to develop the models. Results support the “safety in numbers” hypothesis and show the significant impact of bike network indicators on cyclist safety.

    Abstract Many cities worldwide are recognizing the important role that cycling plays in creating green and livable communities. However, vulnerable road users such as cyclists are usually subjected to an elevated level of injury risk which discourages many road users to cycle. This paper studies cyclist-vehicle collisions at 134 traffic analysis zones in the city of Vancouver to assess the impact of bike network structure on cyclist safety. Several network indicators were developed using Graph theory and their effect on cyclist safety was investigated. The indicators included measures of connectivity, directness, and topography of the bike network. The study developed several macro-level (zonal) collision prediction models that explicitly incorporated bike network indicators as explanatory variables. As well, the models incorporated the actual cyclist exposure (bike kilometers travelled) as opposed to relying on proxies such as population or bike network length. The macro-level collision prediction models were developed using generalized linear regression and full Bayesian techniques, with and without spatial effects. The models showed that cyclist collisions were positively associated with bike and vehicle exposure. The exponents of the exposure variables were less than one which supports the “safety in numbers” hypothesis. Moreover, the models showed positive associations between cyclist collisions and the bike network connectivity and linearity indicators. In contrast, negative associations were found between cyclist collisions and the bike network continuity and topography indicators. The spatial effects were statistically significant in all of the developed models.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models


    Beteiligte:
    Osama, Ahmed (Autor:in) / Sayed, Tarek (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2016-08-06


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch