A novel approach to set driving simulator experiments based on traffic crash data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Mariane Bobermin (Autor) Outra; Sara Ferreira (Autor) FEUP

Revista

Título: Accident Analysis and Prevention

Vol. 150

ISSN: 0001-4575

Editora: Elsevier

Indexação

ISI Web of Knowledge - 1 Citação

ISI Web of Science

Scopus - 2 Citações

Pubmed / Medline

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia civil

FOS: Ciências da engenharia e tecnologias > Engenharia civil

Outras Informações

ID Authenticus: P-00T-7HS

DOI: 10.1016/j.aap.2020.105938

Abstract (EN): Several studies have often cited crash occurrences as a motivation to perform a driving simulator experiment and test driver behavior to understand their causal relations. However, decisions regarding the simulated scenario and participants' requirements do not often rely directly on traffic crash data. To fill the gap between simulation and real data, we have proposed a new framework based on Clustering Analysis (K-medoids) to support the definition of driving simulator experiments when the purpose is to investigate the driver behavior under real risky road conditions to improve road safety. The suggested approach was tested with data of three years of police records regarding loss-of-control crashes and information on three Brazilian rural highways' geometry and traffic volume. The results showed the good suitability of the method to compile the data's diversity into four clusters, representing and summarizing the crashes' main characteristics in the region of study. Drivers' attributes (age and gender) were initially intended to integrate the clustering analysis; however, due to the sample's homogeneity of these characteristics, they did not contribute to the cluster definition. Hence, they were used simply to identify the target population for all scenarios. Therefore, we concluded that driving simulator experiments could benefit from the new approach since it identifies scenarios characterized by many variables connected to real risky situations and orients participants' recruitment leading to efficient safety analysis.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8