Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms

Vanderlei Carneiro Silva, Aluane Silva Dias, Julia Maria D’Andréa Greve, Catherine L. Davis, André Luiz de Seixas Soares, Guilherme Carlos Brech (), Sérgio Ayama, Wilson Jacob-Filho, Alexandre Leopold Busse, Maria Eugênia Mayr de Biase, Alexandra Carolina Canonica and Angelica Castilho Alonso
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Vanderlei Carneiro Silva: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Aluane Silva Dias: Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
Julia Maria D’Andréa Greve: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Catherine L. Davis: Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, GA 30901, USA
André Luiz de Seixas Soares: Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
Guilherme Carlos Brech: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Sérgio Ayama: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Wilson Jacob-Filho: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Alexandre Leopold Busse: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Maria Eugênia Mayr de Biase: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Alexandra Carolina Canonica: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Angelica Castilho Alonso: Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil

IJERPH, 2023, vol. 20, issue 5, 1-13

Abstract: The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time ( p < 0.05). The random forest performed well (r = 0.98, R 2 = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.

Keywords: safe driving; older drivers; crash risk; clustering analysis; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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