Preface

This book begins with the basics of the Computational Intelligence technique and introduces its applications to vehicular traffic prediction, optimization, behaviour 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 behaviour which is called Computational Intelligence (CI) technique. The CI technique includes Multiagent system (MAS), Whale optimization, AIS, Deep Neural Networks (DNNs), Fog, and Edge Computation. These CI techniques mimic human behaviour 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 behaviour 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.

OBJECTIVE OF THE BOOK

The objective of this book is to provide a detailed understanding of the Computational Intelligence techniques with the main focus on sustainable transportation and mobility field. The final goal is to connect and utilize the new CI techniques to interdisciplinary areas that can be put to good use.

ORGANIZATION OF THE BOOK

The book is organized into 8 chapters with the following brief description:

1. An Intelligent Vehicular Traffic Flow Prediction Model using Whale Optimization with Multiple Linear Regression

In this chapter, the authors introduce an Intelligent Vehicular Traffic Flow Prediction (IVTFP) model to predict the flow of traffic on the road effectively. The proposed IVTFP model involves two mains stages: 1. Feature selection and classification and 2. Multiple linear regression technique.

2. Intelligent Transportation Systems based Behaviour Characteristics Classification

In this chapter, a layout of Rule-Based Fuzzy Polynomial Neural Networks system based on their behavior in different profiles to regulate the behaviour of the drivers is presented. The work has developed a probability model to show the observer rating works relatively well with the sophisticated models.

3. Artificial Immune Systems Imputation based Traffic Prediction

In this chapter, an AIS algorithms based traffic prediction is designed for long-term root prediction. Predictive assessments of data sensitivity are data on urban data flow. The simulation results show that the proposed sequence achieves the same accuracy as arbitrary predictions and fewer blocks than conventional solutions.

4. An Intelligent Transportation Systems for Traffic Density Estimation and Prediction using Deep Learning Models

This chapter develops a new deep learning (DL) based traffic density estimation and prediction model for ITS. The proposed model involves a set of two DL models, namely convolutional neural network (CNN) and long short term memory (LSTM) for traffic density estimation and prediction.

5. Fog and Edge Computing based Intelligent Transport System

This chapter focuses on reducing the latency with advanced algorithms in data transferring between the fog and edge layers. It discusses its application and the internal processing in the fog layers with advanced algorithms.

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

This chapter constructs smart pavements for the future use of introducing autonomous vehicles to make traveling safer and comfortable for the people. This chapter focuses on the technologies being used for the integration of sensors with DAQ devices via wireless communication technologies in the transportation network.

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

This chapter aims to counter all the disadvantages by presenting a simulation-based study on standalone Solar DC microgrid for electric vehicle charging. This can be used in the current Indian energy scenario. The usability of the proposed system in the conventional grid is verified by implementing with IEEE 5 bus system.

8. Industry 4.0: Hyperloop Transportation System in India

Hyperloop is a new, better, and more efficient mode of transportation that is being proposed in this chapter as an alternative to India’s railway and airport network with the benefit of better and more efficient performance at lower overall costs.

About the Book

The exponential growth of vehicles and the human population (especially in the urban/metropolitan area) results in many challenges while information collecting, processing, predicting, and integrating various intelligent technologies. To furnish daily work and lead life, everyone is directly dependent upon transportation, which is inter-related to traffic density, mobility, traffic demands, etc. In urban areas, the traffic demands have grown faster than the construction of required infrastructure, reduced the mobility of vehicles, and increased traffic congestion, which is one of the serious problems each city in the country is facing. In addition, the increase in the vehicles population, traffic congestion, etc., leads to degradation of the environment and unnecessary wastage of fuel. These have become serious concerns for the public. This book begins with the basics of the Computational Intelligence techniques required for sustainable transportation and mobility. 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 an intelligent behavior which is called Computational Intelligence (CI) technique. The CI technique includes Multiagent 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. This book presents novel techniques developed, new technologies, and computational intelligence for sustainable mobility and transportation data prediction, traffic behaviour analysis, traffic density estimation and prediction, electric vehicles charging infrastructure, and Industry 4.0.