Exploratory analysis of crash determinants

Maike Metz-Peeters and Jil-Laurel Patragst

No 1157, Ruhr Economic Papers from RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen

Abstract: This study presents an exploratory analysis of the key factors contributing to fatal and severe crashes on German motorways. We employ Poisson and Negative Binomial regression models, combined with Lasso regularization and stability selection, to explore model specifications incorporating potentially many interaction terms and polynomials. Utilizing an extensive data set including rich geo-spatial characteristics for 500-meter segments covering large parts of the German motorway network, key variables influencing crash frequency are uncovered. To obtain correct standard errors post variable selection, we split the data into separate samples for model selection and parameter estimation. Our results indicate that the inclusion of a limited number of higher-order terms significantly improves the regression formulation. Robustness checks confirm the stability of these findings. The results offer clearer insights into the key crash determinants and are more computationally feasible than simulation-based methods commonly used in accident research.

Keywords: Road safety; crash frequency; lasso regression; machine learning; stability selection (search for similar items in EconPapers)
JEL-codes: C52 H10 R41 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:rwirep:319076

DOI: 10.4419/96973341

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