Computational intelligence for sustainable transportation and mobility / edited by Deepak Gupt and Suresh Chavhan.

Format:

Book

Contributor:

Gupt, Deepak, editor.

Chavhan, Suresh, editor.

Series:

Computational intelligence for data analysis ; v. 1.

Computational intelligence for data analysis ; v. 1

Language:

English

Subjects (All):

Computational intelligence.

Transportation--Research--Computer-aided design.

Transportation.

Transportation--Planning--Computer-aided design.

Physical Description:

1 online resource (145 pages)

Edition:

1st ed.

Place of Publication:

UAE : Bentham Science Publishers, [2021]

Summary:

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.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

List of Contributors

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

Hima Bindu Gogineni1, E. Laxmi Lydia2,* and N. Supriya3

INTRODUCTION

THE PROPOSED IVTFP MODEL

WOA Based Feature Selection Model

Prey Encircling

Exploitation Phase

Exploration Phase

MLR Based Predictive Model

PERFORMANCE VALIDATION

Dataset Description

Results Analysis

CONCLUSION

CONSENT FOR PUBLICATION

CONFLICTS OF INTEREST

ACKNOWLEDGEMENTS

REFERENCES

Intelligent Transportation Systems-based Behavior Characteristics Classification

B.M.S. Rani1, E. Laxmi Lydia2,* and G. Jose Moses3

LITERATURE SURVEY

PROPOSED METHODOLOGY

Intelligent Transportation Systems

Normal Behavior

Drunk Behavior

Fatigue Behavior

Reckless Behavior

Driver Information and Behavior

Traveler Information and Network Behavior

Rule-Based Fuzzy Polynomial Neural Network

RESULT AND DISCUSSION

Artificial Immune Systems Imputation-based Traffic Prediction

M. Vasumathi Devi1, E. Laxmi Lydia2,* and Hima Bindu Gogineni3

Openflow Based Software-defined Optical Network

Artificial Immune System

RESULTS AND DISCUSSION

CONFLICT OF INTEREST

An Intelligent Transportation System for Traffic Density Estimation and Prediction Using Deep Learning Models

Irina V. Pustokhina1, Denis A. Pustokhin2, M. Ilayaraja3 and K. Shankar4,*

THE PROPOSED MODEL

CNN Model

LSTM Model

Constant Error Carousel (CEC).

Input Gate

Output Gate

Input

Input Gate

Forget Gate

Memory Cell

Output

Analysis of Density Estimation

Analysis of Density Prediction

Fog and Edge Computing-based Intelligent Transport System

B. Sai Viswanath1,*, P. Sandeep1 and Suresh Chavhan2

Fog Computing Overview

Characteristics of Fog

Fog Working

Algorithm I

Edge Computing Overview

Characteristics of Edge Computing

Computing Offloading

Processing

Caching

Data Storage

Intelligent Transportation System

RELATED WORKS

IMPLEMENTING ITS WITH FOG AND EDGE COMPUTING (PROTOTYPE)

Algorithm - II

Advantages of the Prototype

CHALLENGES [16]

IoT-based Integration of Sensors with DAQ Systems in Intelligent Transport Systems

Dhananjay Kumar K.S.1, Prakash Reddy O.1, Sanath Gowtham G.1, Shailaja A. Chougule1 and Suresh Chavhan2

Transportation Networks and Intelligent Transportation System

Advanced Traffic Management Systems

Advance Parking Management Systems

Advance Lane Management System

METHODOLOGY

Sensors

DAQ Systems

Big Data Analytics

Cloud Computing

FUTURE WORKS

Solar-based Electric Vehicle Charging Infrastructure with Grid Integration and Transient Overvoltage Protection

Bibaswan Bose1,*, Vijay Kumar Tayal1 and Bedatri Moulik1

MATHEMATICAL MODELING

Solar PV Array

Boost Converter

Battery

Supercapacitor

Three-phase AC Inverter

Three-phase Induction Motor.

IEEE 5 Bus System

PID Controller

SYSTEM ARCHITECTURE

Modes of Operation

SIMULATION RESULTS

Three-phase Induction Motor (IM) Load

IEEE 5 Bus system Load

Transient Overvoltage Protection

Industry 4.0: Hyperloop Transportation System in India

Pranjal Kapur1,* and Suresh Chavhan2

Capsule

Tube

Propulsion

Route

DETAILED VIEW OF THE HYPERLOOP PASSENGER CAPSULE

HOW DOES THE HYPERLOOP TRANSPORTATION SYSTEM WORK?

COST ANALYSIS OF HYPERLOOP TRANSPORTATION SYSTEM IN INDIA

SAFETY AND RELIABILITY OF THE HYPERLOOP TRANSPORTATION SYSTEM

Onboard Passenger Emergency

Power Outage

Capsule Depressurization

Earthquakes

COMMUNICATION TECHNOLOGIES FOR HYPERLOOP

RENEWABILITY OF THE HYPERLOOP TRANSPORTATION SYSTEM

COMPARISON BETWEEN DIFFERENT MODES OF PUBLIC TRANSPORTATION

FUTURE PLANS FOR HYPERLOOP TRANSPORTATION SYSTEM IN INDIA

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print record.

Includes bibliographical references.

ISBN:

9781681089430

1681089432

OCLC:

1293249742