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.
Predicting Bicycle-Involved Crashes in the SCAG Region: A Machine Learning Analysis Using HSIS Data from California State
International Conference on Transportation and Development 2024 ; 2024 ; Atlanta, Georgia
13.06.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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