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About Data Analytics and Machine Learning
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. The advent of Machine Learning has revolutionized data analysis, pattern recognition, and predictive modeling across various domains, including transportation and traffic management. Among the myriad of techniques, clustering, outlier detection, and neural networks stand out for their ability to uncover patterns, identify anomalies, and predict outcomes from complex datasets.
Detailed Information
| Author: | Yashaswi Karnati |
|---|---|
| Language: | English |
| File Size: | 11 |
| Format: | |
| Price: | FREE |
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