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.
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
Table of Contents
Chapter 1 An Intelligent Vehicular Traffic Flow Prediction Model Using Whale Optimization With Multiple Linear Regression- Hima Bindu Gogineni, E. Laxmi Lydia And N. Supriya
- 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
Chapter 2 Intelligent Transportation Systems-Based Behavior Characteristics Classification
- B.M.S. Rani, E. Laxmi Lydia And G. Jose Moses
- Introduction
- 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
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
Chapter 3 Artificial Immune Systems Imputation-Based Traffic Prediction
- M. Vasumathi Devi, E. Laxmi Lydia And Hima Bindu Gogineni
- Introduction
- Literature Survey
- Proposed Methodology
- Openflow Based Software-Defined Optical Network
- Artificial Immune System
- Results And Discussion
- Conclusion
- Consent For Publication
- Conflict Of Interest
- Acknowledgements
- References
Chapter 4 An Intelligent Transportation System For Traffic Density Estimation And Prediction Using Deep Learning Models
- Irina V. Pustokhina, Denis A. Pustokhin, M. Ilayaraja And K. Shankar
- Introduction
- The Proposed Model
- Cnn Model
- Lstm Model
- Constant Error Carousel (Cec)
- Input Gate
- Output Gate
- Input
- Input Gate
- Forget Gate
- Memory Cell
- Output Gate
- Output
- Performance Validation
- Analysis Of Density Estimation
- Analysis Of Density Prediction
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
Chapter 5 Fog And Edge Computing-Based Intelligent Transport System
- B. Sai Viswanath, P. Sandeep And Suresh Chavhan
- Introduction
- 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]
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
Chapter 6 Iot-Based Integration Of Sensors With Daq Systems In Intelligent Transport Systems
- Dhananjay Kumar K.S., Prakash Reddy O., Sanath Gowtham G., Shailaja A. Chougule And Suresh Chavhan
- Introduction
- Transportation Networks And Intelligent Transportation System
- Related Works
- Advanced Traffic Management Systems
- Advance Parking Management Systems
- Advance Lane Management System
- Methodology
- Sensors
- Daq Systems
- Big Data Analytics
- Cloud Computing
- Future Works
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
Chapter 7 Solar-Based Electric Vehicle Charging Infrastructure With Grid Integration And Transient Overvoltage Protection
- Bibaswan Bose, Vijay Kumar Tayal And Bedatri Moulik
- Introduction
- 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
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
Chapter 8 Industry 4.0: Hyperloop Transportation System In India
- Pranjal Kapur And Suresh Chavhan
- Introduction
- 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
- Related Works
- Conclusion
- Consent For Publication
- Conflicts Of Interest
- Acknowledgements
- References
- Subject Index
Author
- Deepak Gupt
- Suresh Chavhan
