Foued Saadaoui – författare

Advanced Statistical Methods for Transportation Economics and Engineering presents recent advances and contemporary approaches in statistics, machine learning, and computational modeling, catered to the evolving demands of transportation systems. It analyses and interrogates smart statistical tools — like machine learning, time-series analysis, and spatial statistics — to help make sense of complex and heterogeneous transport systems.The fields of transportation economics and engineering are undergoing rapid transformation, driven by advancements in transportation systems, emerging technologies, and the imperative for sustainable solutions. To navigate these changes effectively, professionals and researchers require robust statistical tools to analyze the complex interplay of the economic, social, and engineering factors that are shaping modern transportation. Bridging the gap between theory and practice, this book emphasizes innovative approaches, interdisciplinary perspectives, and real-world applications. Emphasis is placed on interpretability and actionable results, ensuring that technical developments translate into practical improvements in transport systems.With contributions from a range of leading interdisciplinary experts and a plethora of real-world case studies, academics, practitioners, and policymakers in transport, sustainability, logistics, and urban planning will find within these pages a crucial resource for applying advanced analytical techniques to the numerous economic and engineering challenges shaping the future of transportation.

This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance.It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks