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Identifying the latent relationships between factors associated with traffic crashes through graphical models.

Authors :
Ulak, Mehmet Baran
Ozguven, Eren Erman
Source :
Accident Analysis & Prevention. Mar2024, Vol. 197, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• Identifying the latent relationships between factors associated with traffic crashes through Markov random fields: Graphical models disclosed relationship topologies of factors leading to severe crashes. • The crash mechanisms illustrated by using graphical representation of relationships. • High dimensionality arising in case of rare crash types can be handled by GLE. • Essential factors jointly acting towards crash occurrence can be identified, similar to a pathological examination. • Proposed approaches can assist in devising accurate and reliable prevention measures. Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on the problem at hand. Despite the importance and success of this approach, there are two issues that are generally not discussed: 1) the latent relationships between factors associated with crashes are oftentimes not the focus of analysis or not observed; and 2) there are not many tools to make informed decisions on which variables might have an impact on the crash outcome and should be included in a safety model, particularly when observations are limited. To address these issues, this paper proposes the use of graphical models, namely a Markov random field (MRF) modelling, Bayesian network modelling, and a graphical XGBoost approach, to disclose relationship topologies of explanatory variables leading to fatal and incapacitating injury pedestrian crashes. The application of graph learning models in traffic safety has a high potential because they are not only useful to understand the mechanism behind the crash occurrence but also can assist in devising accurate and reliable prevention measures by identifying the true variable structure and essential factors jointly acting towards crash occurrence, similar to a pathological examination. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00014575
Volume :
197
Database :
Academic Search Index
Journal :
Accident Analysis & Prevention
Publication Type :
Academic Journal
Accession number :
175007942
Full Text :
https://doi.org/10.1016/j.aap.2024.107470