Machine Learning Techniques for Classification of Stress Levels in Traffic
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...
Saved in:
Main Authors: | Amanda Trojan Fenerich, Egídio José Romanelli, Rodrigo Eduardo Catai, Maria Teresinha Arns Steiner |
---|---|
Format: | Article |
Language: | English |
Published: |
Universidade Federal de Pernambuco (UFPE)
2024-06-01
|
Series: | Socioeconomic Analytics |
Subjects: | |
Online Access: | https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/262686 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Using deep learning model integration to build a smart railway traffic safety monitoring system
by: Chin-Chieh Chang, et al.
Published: (2025-02-01) -
Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset
by: Omid Kohandel Gargari, et al.
Published: (2025-02-01) -
An Intelligent Solar-Powered Traffic Lights that Monitor Traffic Flow Using Sensors and Adjust Signal Timings Based on Vehicle Density: A Case Study of Kampala-Uganda.
by: Asinguza, Blessed, et al.
Published: (2024) -
ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment
by: Marco Guerrieri, et al.
Published: (2025-01-01) -
Traffic flow modelling of vehicles on a six lane freeway: Comparative analysis of improved group method of data handling and artificial neural network model
by: Isaac Oyeyemi Olayode, et al.
Published: (2025-03-01)