This study investigates factors influencing the severity of bicycle-involved crashes in the US, particularly the Southern California Association of Governments (SCAG) region from 2013 to 2017, using Highway Safety Information System (HSIS) data. Employing a Bayesian network model with rigorous validation, the study yields a low error rate, emphasizing its effectiveness in analyzing crash data and enhancing rider safety insights. Two scenarios explore variables affecting the probability of fatal crashes, revealing the positive impact of proper lighting and surface on visibility. Additionally, the need for infrastructure capable of handling wet surfaces and providing adequate drainage is underscored. The results demonstrate the importance of effective infrastructure design, emphasizing proper lighting and visibility for cyclists to mitigate fatal crash risks. The study’s implications extend to informing policymakers and transportation engineers on prioritizing safety measures, showcasing the Bayesian network model’s efficacy in identifying critical factors in bicycle-involved crashes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting Bicycle-Involved Crashes in the SCAG Region: A Machine Learning Analysis Using HSIS Data from California State


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2024 ; 2024 ; Atlanta, Georgia



    Erscheinungsdatum :

    13.06.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Damage to bicycle helmets involved with crashes

    Ching, Randal P. | Online Contents | 1997


    Hit and run crashes: Knowledge extraction from bicycle involved crashes using first and frugal tree

    Subasish Das / Anandi Dutta / Xiaoqiang Kong et al. | DOAJ | 2019

    Freier Zugriff

    Predicting throw distance variations in bicycle crashes

    Mukherjee,S. / Chawla,A. / Mohan,D. et al. | Kraftfahrwesen | 2006


    Statewide Analysis of Bicycle Crashes

    P. Alluri / M. A. Raihan / D. Saha et al. | NTIS | 2017