Exploring commuter stress dynamics through machine learning and double optimization

Travel dynamics significantly impact commuter stress, influenced by traffic behavior, road conditions, travel modes, distance, and socio-demographic characteristics. Previous research on travel stress often exhibits limitations, including narrow scopes focusing on specific routes, vehicle types, or demographics. This study addresses these constraints by employing a comprehensive approach to analyze the influence of various travel attributes on commuter stress levels. An interview-based dataset was collected to capture the multifaceted experiences of road users. Five tree-based machine learning models–Decision Tree (DT), Random Forests (RF), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (k-NN)–were deployed for imbalanced multi-class classification. XGBoost demonstrated superior performance with the highest accuracy (73.33%) and precision (75.63%) with a standard deviation of ±5.9. A novel double hyperparameter optimization technique enhanced the prediction accuracy across all models, notably increasing the k-NN classifier’s accuracy to 19.99%. The SHAP (SHapley Additive exPlanations) method was utilized for model interpretability, revealing distance traveled per day as the most influential factor across stress levels, followed by mode of transport, gender, and age for low, medium, and high-stress categories, respectively. The study also examines the impact of features on individual commuter stress levels through random instance selection. This research provides valuable insights into the complex interplay between travel attributes and commuter stress, paving the way for the development of effective stress mitigation strategies and improved travel experiences for all road users.