240 Pages 85 Color Illustrations
    by CRC Press

    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.


    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



    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