Data Analytics and Machine Learning for Integrated Corridor Management
1st Edition
Will be published approx. on 22. June 2026
Book
Paperback/Softback
216 pages
978-1-032-57471-4 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic and Postgraduate
Illustrations
85 farbige Abbildungen, 10 Farbfotos bzw. farbige Rasterbilder, 75 farbige Zeichnungen
75 Line drawings, color; 10 Halftones, color; 85 Illustrations, color
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 13 mm
Weight
345 gr
ISBN-13
978-1-032-57471-4 (9781032574714)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yashawi Karnati | Dhruv Mahajan | Tania Banerjee
Data Analytics and Machine Learning for Integrated Corridor Management
E-Book
10/2024
1st Edition
CRC Press
€63.49
Available for download
Yashawi Karnati | Dhruv Mahajan | Tania Banerjee
Data Analytics and Machine Learning for Integrated Corridor Management
E-Book
10/2024
1st Edition
CRC Press
€63.49
Available for download
Yashawi Karnati | Dhruv Mahajan | Tania Banerjee
Data Analytics and Machine Learning for Integrated Corridor Management
Book
10/2024
1st Edition
CRC Press
€145.60
Shipment within 10-20 days
Persons
Yashaswi Karnati is a computer scientist with expertise in machine learning, computer vision and intelligent transportation systems. Having completed a PhD in Computer Science from the University of Florida, he has embarked on a career that sits at the intersection of academic excellence and industry innovation. He is currently working with NVIDIA Corporation, focusing on the development of digital twin technologies.
Dhruv Mahajan completed his Ph.D. from the Department of Computer & Information Science & Engineering, University of Florida in May 2021. He is currently working on advancing Privacy Preserving Machine Learning techniques at Procter & Gamble.
Tania Banerjee serves as a Research Assistant Scientist within the Department of Computer & Information Science & Engineering at the University of Florida. Her research interests are in the area
Rahul Sengupta is a Ph.D. student at the Computer and Information Science Department at the University of Florida, Gainesville, USA. His research interests include applying Machine Learning models to sequential and time-series data, especially in the field of transportation engineering.
Clay Packard is a principal software and systems engineer at HNTB with a focus in transportation technology. Clay provides technical leadership in systems planning, program and project development, and providing subject matter expertise to transportation agencies.
Ryan Casburn, a traffic engineer with over five years of experience, boasts a lifelong passion for optimizing transportation systems. Fascinated by the dynamic interactions within these systems, he specializes in crafting practical solutions based on real-world behaviors rather than purely theoretical models. His diverse project portfolio spans microsimulation, signal retiming, and transportation planning, software development of user-friendly transportation analysis tools. Ryan's expertise and dedication make him a valuable asset in the realm of traffic engineering and integrated corridor management.
Anand Rangarajan is Professor, Dept. of CISE, University of Florida. His research interests are machine learning, computer vision, medical and hyperspectral imaging and the science of consciousness.
Jeremy Dilmore is the Transportation Systems Management and Operation Engineer for the Florida Department of Transportation District 5. He has 19 years of experience with the Department, with 13 of those years in Intelligent Transportation Systems and/or Transportation Systems Management and Operations. In this position he is responsible for leadingFDOTDistrict 5's technology efforts including working in the fields of signal timing optimization, managed lanes, simulation modeling, and connected and autonomous vehicles.
Sanjay Ranka is a Distinguished Professor in the Department of Computer Information Science and Engineering at University of Florida. His current research interests are high performance computing and big data science with a focus on applications in CFD, healthcare and transportation. He has co-authored four books, 290+ journal and refereed conference articles. He is a Fellow of the IEEE and AAAS. He is an Associate Editor-in-Chief of the Journal of Parallel and Distributed Computing and an Associate Editor for ACM Computing Surveys, Applied Sciences, Applied Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics
Dhruv Mahajan completed his Ph.D. from the Department of Computer & Information Science & Engineering, University of Florida in May 2021. He is currently working on advancing Privacy Preserving Machine Learning techniques at Procter & Gamble.
Tania Banerjee serves as a Research Assistant Scientist within the Department of Computer & Information Science & Engineering at the University of Florida. Her research interests are in the area
Rahul Sengupta is a Ph.D. student at the Computer and Information Science Department at the University of Florida, Gainesville, USA. His research interests include applying Machine Learning models to sequential and time-series data, especially in the field of transportation engineering.
Clay Packard is a principal software and systems engineer at HNTB with a focus in transportation technology. Clay provides technical leadership in systems planning, program and project development, and providing subject matter expertise to transportation agencies.
Ryan Casburn, a traffic engineer with over five years of experience, boasts a lifelong passion for optimizing transportation systems. Fascinated by the dynamic interactions within these systems, he specializes in crafting practical solutions based on real-world behaviors rather than purely theoretical models. His diverse project portfolio spans microsimulation, signal retiming, and transportation planning, software development of user-friendly transportation analysis tools. Ryan's expertise and dedication make him a valuable asset in the realm of traffic engineering and integrated corridor management.
Anand Rangarajan is Professor, Dept. of CISE, University of Florida. His research interests are machine learning, computer vision, medical and hyperspectral imaging and the science of consciousness.
Jeremy Dilmore is the Transportation Systems Management and Operation Engineer for the Florida Department of Transportation District 5. He has 19 years of experience with the Department, with 13 of those years in Intelligent Transportation Systems and/or Transportation Systems Management and Operations. In this position he is responsible for leadingFDOTDistrict 5's technology efforts including working in the fields of signal timing optimization, managed lanes, simulation modeling, and connected and autonomous vehicles.
Sanjay Ranka is a Distinguished Professor in the Department of Computer Information Science and Engineering at University of Florida. His current research interests are high performance computing and big data science with a focus on applications in CFD, healthcare and transportation. He has co-authored four books, 290+ journal and refereed conference articles. He is a Fellow of the IEEE and AAAS. He is an Associate Editor-in-Chief of the Journal of Parallel and Distributed Computing and an Associate Editor for ACM Computing Surveys, Applied Sciences, Applied Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics
Content
Chapter 1 Introduction
Chapter 2 Traffic Engineering and operations background
Chapter 3 Integrated Corridor Management System
Chapter 4 Traffic Data Modalities
Chapter 5 Data Mining and Machine Learning
Chapter 6 Traffic Simulation Frameworks for Data Generation
Chapter 7 Intersection Detector Diagnostics
Chapter 8 Intersector Performance
Chapter 9 Interruption Detection
Chapter 10 Estimating Turning Movement Counts
Chapter 11 Coordinating Corridors
Chapter 12 Modeling Input Output Behavior of Intersection
Chapter 13 Modeling Measures of Effectiveness for Intersection Performance
Chapter 14 Signal Timing Optimizations
Chapter 15 Visualisation of Traffic Data
Chapter 2 Traffic Engineering and operations background
Chapter 3 Integrated Corridor Management System
Chapter 4 Traffic Data Modalities
Chapter 5 Data Mining and Machine Learning
Chapter 6 Traffic Simulation Frameworks for Data Generation
Chapter 7 Intersection Detector Diagnostics
Chapter 8 Intersector Performance
Chapter 9 Interruption Detection
Chapter 10 Estimating Turning Movement Counts
Chapter 11 Coordinating Corridors
Chapter 12 Modeling Input Output Behavior of Intersection
Chapter 13 Modeling Measures of Effectiveness for Intersection Performance
Chapter 14 Signal Timing Optimizations
Chapter 15 Visualisation of Traffic Data