[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126169-en":3,"doc-seo-126169-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126169,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Exploring commuter stress dynamics through machine learning and double optimization","Travel dynamics strongly shape commuter stress through traffic behavior, road conditions, transport modes, trip distance, and socio-demographic factors. Prior studies often constrain the problem with narrow settings focused on limited routes, vehicle types, or participant groups. This study uses an interview-based dataset capturing road users’ experiences, then trains five tree-based machine learning models for imbalanced multi-class stress prediction. XGBoost achieves the best performance and a double hyperparameter optimization strategy further boosts accuracy. SHAP interpretability identifies daily traveled distance as most influential across stress categories, followed by transport mode, gender, and age.","[https://doi.org/10.22581/muet1982.0062](https://doi.org/10.22581/muet1982.0062)  \n2025, 44(2) 35-46  \nExploring commuter stress dynamics through machine learning and double optimization  \nAshar Ahmed a, * , Mario Munoz-Organero b, Bushra Aijaz c  \na Department of Urban and Infrastructure Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan  \nb Universidad Carlos III de Madrid, Av. Universidad, 30, 28911 Leganés, Madrid, Spain c Independent Researcher, Karachi 75290, Sindh, Pakistan  \n* Corresponding author: Ashar Ahmed, Email: [aahmed@cloud.neduet.edu.pk](aahmed@cloud.neduet.edu.pk)  \nReceived: 06 January 2025, Accepted: 27 March 2025, Published: 01 April 2025  \nK E Y W O R D S A B S T R A C T  \nDouble optimization  \nImbalanced dataset  \nMachine learning Road safety SHAP  \nStress  \nTravel 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 ofeffective stress mitigation strategies and improved travel experiences for all road users.  \n1. Introduction  \nKarachi, the largest city in Pakistan and ranked among the top 10 most populous metropolitan areas globally, spans approximately 560 square miles and is home to an estimated 15 million people as of 2017 [1, 2] . This sprawling urban center features a diverse demographic composition, varied infrastructure, and extreme climatic conditions. The city’s extensive travel  \ndistances have profound implications for commuters’health, with the quality of road infrastructure playing a pivotal role in ensuring their physiological comfort and psychological well-being [3] . The discipline of traffic science is intricately tied to the safety and comfort of road users, emphasizing the need for systematic urban planning and effective traffic management.  \nGood infrastructure and a planned transportation system are vital for a city’s prosperity. Conversely, inadequate infrastructure can adversely affect mental health, exacerbating stress and reducing the quality of life. Studies indicate that traffic congestion and transportation inefficiencies have resulted insignificant psychological and physiological challenges for commuters [4]. Drivers are increasingly exposed to complex traffic scenarios, which complicate the prediction of stress responses [5] .  \nKey factors contributing to these challenges include irregular and narrow roads, tight corners [6], double parking, wrong-way traffic, unsignalized intersection","cbCaikc9t33GRSb4","https://ap.wps.com/l/cbCaikc9t33GRSb4","pdf",625394,5,1,12,"English","en",105,"# Introduction\n## Karachi urban travel context and commuter stress\n## Infrastructure, traffic conditions, and stress mechanisms\n## Commuting modes and differences in stress exposure","[{\"question\":\"What factors influence commuter stress in this research?\",\"answer\":\"The study considers traffic behavior, road conditions, travel mode, distance traveled, and socio-demographic characteristics affecting commuter stress levels.\"},{\"question\":\"How is commuter stress predicted in the study?\",\"answer\":\"Five tree-based machine learning models (DT, RF, ET, XGBoost, and k-NN) are trained for imbalanced multi-class classification, with XGBoost providing the strongest results.\"},{\"question\":\"How does the study interpret which features drive stress categories?\",\"answer\":\"SHAP is used for model interpretability, showing that daily traveled distance is the most influential factor, with transport mode, gender, and age ranking next across low, medium, and high-stress categories.\"}]","Exploring commuter stress dynamics through machine learning and double optimization | 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factors influence commuter stress in this research?","Question",{"text":77,"@type":78},"The study considers traffic behavior, road conditions, travel mode, distance traveled, and socio-demographic characteristics affecting commuter stress levels.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is commuter stress predicted in the study?",{"text":82,"@type":78},"Five tree-based machine learning models (DT, RF, ET, XGBoost, and k-NN) are trained for imbalanced multi-class classification, with XGBoost providing the strongest results.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the study interpret which features drive stress categories?",{"text":86,"@type":78},"SHAP is used for model interpretability, showing that daily traveled distance is the most influential factor, with transport mode, gender, and age ranking next across low, medium, and high-stress 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