TY - JOUR
T1 - Crash Risk Predictors in Older Drivers
T2 - A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms
AU - Silva, Vanderlei Carneiro
AU - Dias, Aluane Silva
AU - Greve, Julia Maria D’Andréa
AU - Davis, Catherine L.
AU - Soares, André Luiz de Seixas
AU - Brech, Guilherme Carlos
AU - Ayama, Sérgio
AU - Jacob-Filho, Wilson
AU - Busse, Alexandre Leopold
AU - de Biase, Maria Eugênia Mayr
AU - Canonica, Alexandra Carolina
AU - Alonso, Angelica Castilho
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - 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, R2 = 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.
AB - 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, R2 = 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.
KW - clustering analysis
KW - crash risk
KW - machine learning
KW - older drivers
KW - safe driving
UR - http://www.scopus.com/inward/record.url?scp=85149875358&partnerID=8YFLogxK
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U2 - 10.3390/ijerph20054212
DO - 10.3390/ijerph20054212
M3 - Article
AN - SCOPUS:85149875358
SN - 1661-7827
VL - 20
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 5
M1 - 4212
ER -