Publication: Identifying linkages between demographics, behaviors, and road accident frequency: a machine learning approach in England
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Date
2023
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AESOP
Abstract
This study addresses the challenge of collecting comprehensive data on individual drivers' behavior, which has been found to contribute to over 70% of road accidents. It focuses on demographic factors and their indirect impact on accident rates by leveraging existing literature on the connection between demographics and risky driving behaviors. A review of literature identifies demographic characteristics correlated with risky driving behaviors. Using regression-based machine learning models, the investigation covers all of England, UK, aiming to establish connections between driving behaviors, demographics, and accident frequency. The study's results align with previous findings and provide a valuable methodology to investigate behavior-accident links on a broader scale despite data limitations. The findings support utilizing readily available demographic information to estimate road accident rates.
Keywords:Driving behavior; Traffic accidents; Cohort analysis; SHAP values; Extra Trees Regressor
Description
Book of proceedings: 35th AESOP Annual Congress Integrated planning in a world of turbulence, Łódź, 11-15th July, 2023
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CC BY 4.0