The book “Computational Intelligence for Sustainable Transportation and Mobility - Volume 1“ begins with the basics of the Computational Intelligence technique and introduces its applications to vehicular traffic prediction, optimization, behavior analysis, traffic density estimation, etc. New technologies and methodologies are used to improve the existing issues of the traffic system. Due to the development of computational intelligence methods, it is considered a powerful technique to reduce the traffic, transportation, and mobility problems in urban areas. In dynamic and complex situations, an adaptive mechanism is required to enable or facilitate intelligent behavior which is called Computational Intelligence (CI) technique.
The CI technique includes Multi agent system (MAS), Whale optimization, AIS, Deep Neural Networks (DNNs), Fog, and Edge Computation. These CI techniques mimic human behavior and intelligence; therefore, the concept of intelligence directly links to reasoning and decision making. These CI techniques are used to develop algorithms, models, and approaches for sustainable transportation, traffic, and mobility operations.
The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation data prediction, traffic behavior analysis, traffic density estimation and prediction, electric vehicles charging infrastructure, and Industry 4.0. The primary emphasis of this book is to introduce computational intelligence techniques, challenges, issues, and concepts to researchers, scientists, and academicians at large.
Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. This volume is essential reading for scholars and professionals involved in courses and training programs in the field of transportation, computer science, data science and applied machine learning.
About the Authors:
Deepak Gupta received a B.Tech. from the GGGSIPU, India. He received M.E. from Delhi Technological University, India and Ph.D. from Dr. APJ Abdul Kalam Technical University (AKTU), India. He has completed his Postdoc from the National Institute of Telecommunications, Brazil. He has co-authored more than 231 articles, including 129 SCI papers. He has authored/edited 59 books, published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, DeGruyter and Katsons. He has filled 6 Indian patents. He is a convener of ICICC, ICDAM & DoSCI Springer conferences series. He is the recipient of the 2021 IEEE System Council Best Paper Award. He has been featured in the list of top 2% scientist/researcher database in the world [Table-S7-singleyr-2019]. He is also a series editor of “Elsevier Biomedical Engineering” at Elsevier, “Intelligent Biomedical Data Analysis” at De Gruyter, and “Explainable AI (XAI) for Engineering Applications” at CRC Press.
Dr. Suresh Chavhan (SMIEEE) is an Assistant Professor with the Automotive Research Center, the Vellore Institute of Technology (VIT), Vellore. He was a postdoctoral research fellow who worked at the Federal University of Piaui (UFPI), Brazil. He received his PhD in Electrical Communication Engineering (2019) from the Indian Institute of Science, Bangalore. Before that, he received his Master’s (2013) and Bachelor’s (2011) degrees from the National Institute of Technology, Surathkal and VTU, Belgaum, respectively. He received the 2021 IEEE Madras Section’s Publication Award, the prestigious 2021 IEEE Systems Journal Best Paper Award with $500 prize, and International travel grant from SERB, India. One of his ideas got shortlisted as Top 34 for the prototype building phase of Mercedes-Benz Digital Challenge, India, 2020. He has published more than 30 SCI papers with very good impact factors.
Keywords:
Predictive Modeling, Artificial Immune System, Intelligent Navigation. Data Transfer, Fog and Edge Computing, Autonomous vehicles, Big Data analytics, Cloud computing, Sensors, Transportation networks. Air Bearings, Electromagnetic Accelerators, Transonic, Vacuum.
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