A multivariate-based variable selection framework for clustering traffic conflicts in a brazilian freeway.

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ID: 32590
2019
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Abstract
More than one million people die or suffer non-fatal injuries annually due to road accidents around the world. Understanding the causes that give rise to different types of conflict events, as well as their characteristics, can help researchers and traffic authorities to draw up strategies aimed at mitigating collision risks. This paper proposes a framework for grouping traffic conflicts relying on similar profiles and factors that contribute to conflict occurrence using self-organizing maps (SOM). In order to improve the quality of the formed groups, we developed a novel variable importance index relying on the outputs of the nonlinear principal component analysis (NLPCA) that intends to identify the most informative variables for grouping collision events. Such index guides a backward variable selection procedure in which less relevant variables are removed one-by-one; after each removal, the clustering quality is assessed via the Davies-Bouldin (DB) index. The proposed framework was applied to a real-time dataset collected from a Brazilian highway aimed at allocating traffic conflicts into groups presenting similar profiles. The selected variables suggest that lower average speeds, which are typically verified during congestion events, contribute to conflict occurrence. Higher variability on speed (denoted by high standard deviation, and speed's coefficient of variation levels on that variable), which are also perceived in the assessed freeway near to congestion periods, also contribute to conflicts.
Reference Key
rocha2019aaccident Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rocha, Miriam;Anzanello, Michel;Caleffi, Felipe;Cybis, Helena;Yamashita, Gabrielli;
Journal accident; analysis and prevention
Year 2019
DOI
S0001-4575(19)30533-0
URL
Keywords Keywords not found

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