A novel approach to set driving simulator experiments based on traffic crash data. (February 2021)

• Record Type: Journal Article
• Authors: Bobermin, Mariane; Ferreira, Sara
• Is Part Of: Accident analysis and prevention. Volume 150(2021)
• Publication Date: 2021-02
• Subjects: Driving simulator experiment -- Clustering analysis -- Scenario development -- Road safety; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105938
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Driving simulator experiments are rarely designed based directly on crash data. A new framework based on Clustering Analysis is proposed to support the experiments. The method was successfully applied to three Brazilian rural highways data. Risky road conditions were identified to form the simulated scenarios. Participants' features for the driving simulator experiment were defined. Abstract: 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.