Data Analytics And Machine Learning For Integrated Corridor Management
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Data Analytics and Machine Learning for Integrated Corridor Management
This book takes readers on a journey through the intricate web of contemporary transportation systems, offering unparalleled insights into the strategies, technologies, and methodologies shaping the movement of people and goods in urban landscapes.
Data Analytics and Machine Learning for Integrated Corridor Management
In an era defined by rapid urbanization and ever-increasing mobility demands, effective transportation management is paramount. This book takes readers on a journey through the intricate web of contemporary transportation systems, offering unparalleled insights into the strategies, technologies, and methodologies shaping the movement of people and goods in urban landscapes. From the fundamental principles of traffic signal dynamics to the cutting-edge applications of machine learning, each chapter of this comprehensive guide unveils essential aspects of modern transportation management systems. Chapter by chapter, readers are immersed in the complexities of traffic signal coordination, corridor management, data-driven decision-making, and the integration of advanced technologies. Closing with chapters on modeling measures of effectiveness and computational signal timing optimization, the guide equips readers with the knowledge and tools needed to navigate the complexities of modern transportation management systems. With insights into traffic data visualization and operational performance measures, this book empowers traffic engineers and administrators to design 21st-century signal policies that optimize mobility, enhance safety, and shape the future of urban transportation.
Artificial Intelligence and Machine Learning for Enhancing Resilience: Concepts, Applications, and Future Directions
As contemporary societies face unprecedented challenges such as mounting mental health issues, environmental crises, and socioeconomic insecurity, the urgency of developing objective, scalable, and dynamic methodologies to study resilience has never been greater. This book arises at the intersection of cutting-edge technology and human insight. It focuses on the possibility for AI and ML to transform resilience assessment, prediction, and interventions across the individual, organizational, and ecological levels. The chapters included in this book represent an organized synthesis of cutting-edge science, pragmatic applications, and prospective potential. With machine learning algorithms to estimate psychological resilience and AI-based models for climate change adaptation and ecosystem management, this book demonstrates the rich innovations that are emerging at the cross-sector of technology and resilience science. Perhaps most importantly, this book does not gloss over the urgent ethical, technical, and regulatory issues that arise when AI is introduced to sensitive topics such as mental health and environmental management. Questions about data privacy, algorithmic bias, model interpretability, and equitable technology deployment are thoroughly investigated, providing lessons learned and suggestions for moving ahead. A significant strength of this work is its global focus. Showcasing work from contributors of various methodologies and regions provides the latest views on new methodologies, strategies for practical implementation, and on what still needs to be invented. This guarantees that the publication engages with the messy socio-cultural and environmental contexts in which these interventions work and that it doesn’t just mirror technological possibilities. For academicians, practitioners, technologists, and policymakers, this book is both a fundamental reference and an outlook resource. It provides: Holistic examination of AI and ML in the context of psychological, organizational, and ecological resilience. In-depth reviews on methodological innovations, such as deep learning, natural language processing, and sensor-based assessments. Unprecedented appraisals of barriers to implementation, with ethical and regulatory considerations. We trust that this book will inspire conversation, fuel innovation, and support a future in which technology supplements, rather than replaces, human ability to adapt, recover, and flourish. We encourage readers to critique the content, to reflect on how AI, ML, and resilience intersect in their particular contexts, and to join us in shaping a future where technological and human resilience evolve together.