TY  - JOUR
T1  - Machine Learning Techniques for Classification of Stress Levels in Traffic
A1  - Fenerich, Amanda Trojan
A1  - Romanelli, Egídio José
A1  - Catai, Rodrigo Eduardo
A1  - Steiner, Maria Teresinha Arns
Y1  - 2024///
KW  - Artificial Intelligence
KW  - Machine Learning
KW  - physiological stress
KW  - traffic studies
KW  - Support Vector Machine
KW  - SVM
KW  - Bayesian Networks
KW  - Logistic Regression.
JF  - Socioeconomic Analytics
VL  - 1
IS  - 2
SP  - 84
EP  - 93
DO  - https://doi.org/10.51359/2965-4661.2024.262686
UR  - https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/262686
N2  - The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. The database was obtained through a structured questionnaire with 44 questions, applied to 196 people in the city of Curitiba, PR. The classification algorithms used were Support Vector Machine (SVM), Bayesian Networks (BN), and Logistic Regression (LR), comparatively. The results indicate that the classification of stress levels of new instances (people) as “high” or “low” can be performed using the LR technique (presenting the highest accuracy, 83.67%).
ER  -