This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured.
This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured.
Saeid Pourroostaei Ardakani
Big Data Analytics Transport Systems Machine Learning Techniques Smart Applications Healthcare Healthcare Optimisation Transportation Management Data Science Data Modeling Data Interpretation
"In Big Data Analytics for Smart Transport and Healthcare Systems, the authors present an optimistic view for the future of urban life: one made simpler and safer through the thoughtful integration of digital tools. ... I encourage anyone curious to learn more about data preprocessing and machine learning analysis---particularly for large urban or human datasets---to give the case studies in this book a close read." (Esha Datta, SIAM Review, Vol. 67 (2), May, 2025)