Editors: Deepak Gupta, Suresh Chavhan

Series Title: Computational Intelligence For Data Analysis

Computational Intelligence for Sustainable Transportation and Mobility

Volume 1

eBook: US $49 Special Offer (PDF + Printed Copy): US $59
Printed Copy: US $84
Library License: US $196
ISBN: 978-1-68108-944-7 (Print)
ISBN: 978-1-68108-943-0 (Online)
Year of Publication: 2021
DOI: 10.2174/97816810894301210101

Introduction

New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas.

This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends.

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.

Key Features:

  • - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas
  • - Covers classification of traffic behavior
  • - Demonstrates the application of artificial immune system algorithms for traffic prediction
  • - Covers traffic density estimation using deep learning models
  • - Covers Fog and edge computing for intelligent transportation systems
  • - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers
  • - Presents a current perspective on an urban hyperloop system for India

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.

Preface

- Pp. i-iii (3)
Deepak Gupta, Suresh Chavhan
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List of Contributors

- Pp. iv

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An Intelligent Vehicular Traffic Flow Prediction Model Using Whale Optimization with Multiple Linear Regression

- Pp. 1-15 (15)
Hima Bindu Gogineni, E. Laxmi Lydia*, N. Supriya
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Intelligent Transportation Systems-based Behavior Characteristics Classification

- Pp. 16-31 (16)
B.M.S. Rani, E. Laxmi Lydia*, G. Jose Moses
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Artificial Immune Systems Imputation-based Traffic Prediction

- Pp. 32-48 (17)
M. Vasumathi Devi, E. Laxmi Lydia*, Hima Bindu Gogineni
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An Intelligent Transportation System for Traffic Density Estimation and Prediction Using Deep Learning Models

- Pp. 49-62 (14)
Irina V. Pustokhina, Denis A. Pustokhin, M. Ilayaraja, K. Shankar*
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Fog and Edge Computing-based Intelligent Transport System

- Pp. 63-75 (13)
B. Sai Viswanath*, P. Sandeep, Suresh Chavhan
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IoT-based Integration of Sensors with DAQ Systems in Intelligent Transport Systems

- Pp. 76-88 (13)
Dhananjay Kumar K.S., Prakash Reddy O., Sanath Gowtham G., Shailaja A. Chougule, Suresh Chavhan*
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Solar-based Electric Vehicle Charging Infrastructure with Grid Integration and Transient Overvoltage Protection

- Pp. 89-112 (24)
Bibaswan Bose*, Vijay Kumar Tayal, Bedatri Moulik
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Industry 4.0: Hyperloop Transportation System in India

- Pp. 113-125 (13)
Pranjal Kapur*, Suresh Chavhan
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Subject Index

- Pp. 126-131 (6)

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