Advanced Intelligent Predictive Models for Urban Transportation
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The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments.
- Provides a smart traffic congestion avoidance system with an integrated fuel consumption model.
- Predicts traffic in short-term and regular. This is illustrated with a case study.
- Efficient Traffic light controller and deviation system in accordance with the traffic scenario.
- IoT based Intelligent Transport Systems in a Global perspective.
- Intelligent Traffic Light Control System and Ambulance Control System.
- Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays.
- Bunch of solutions and ideas for smart traffic development in smart cities.
- This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system.
- This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications.
- This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT.
This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.
Table of Contents
1. Overview 2. Related Works 3. Smart Traffic Prediction and Congestion Avoidance System (S-TPCA) Using Genetic Predictive Models for Urban Transportation 4. Short-Term Traffic Prediction Model (STTPM) 5. An Efficient Intelligent Traffic Light Control and Deviation System 6. IoT-Based Intelligent Transportation System (IoT-ITS) 7. Intelligent Traffic Light Control and Ambulance Control System. 8. Conclusions and Future Research. Bibliography. Index.
R. Sathiyaraj is an Assistant Professor in the School of Engineering & Technology at the CMR University, Bangalore, India. He obtained his Ph.D. in Computer Science and Engineering from Anna University, Chennai, India. He is a prominent researcher in the areas of Big Data analytics, machine learning, and the IoT and has published over 15 articles in various top international journals. He holds two authored books and one edited book.
A. Bharathi received her Bachelor’s degree from Bharathiar University, Coimbatore, India and her postgraduate degree from Anna University, Chennai, India. She received her doctoral degree in Information and Communication Engineering, specializing in data mining, from Anna University, Chennai. She has over 22 years of teaching experience. She has published more than 85 research papers in reputed national and international journals, and presented more than 50 technical papers at international/national level conferences.
B. Balamurugan received his B.E. degree in Computer Science and Engineering from Bharathidasan University, Tiruchirappalli, India, in 2001, his M.E. degree in Computer Science and Engineering from Anna University, Chennai, India, in 2005, and his Ph.D. degree in Computer Science and Engineering from VIT University, Vellore, India, in 2015. He is currently a Professor with the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. His current research interests include big data, network security, and cloud computing. He is a Pioneer Researcher in the areas of big data and the IoT and has published over 70 articles in various top international journals.