Advanced Intelligent Predictive Models for Urban Transportation
- Length: 132 pages
- Edition: 1
- Language: English
- Publisher: Chapman & Hall
- Publication Date: 2022-03-28
- ISBN-10: 1032108517
- ISBN-13: 9781032108513
- Sales Rank: #0 (See Top 100 Books)
The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments.
Features:
- Provides a smart traffic congestion avoidance system with an integrated fuel consumption model.
- Predicts traffic in short-term and regular. This is illustrated with a case study.
- Efficient Traffic light controller and deviation system in accordance with the traffic scenario.
- IoT based Intelligent Transport Systems in a Global perspective.
- Intelligent Traffic Light Control System and Ambulance Control System.
- Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays.
- Bunch of solutions and ideas for smart traffic development in smart cities.
- This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system.
- This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications.
- This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.
Cover Half Title Title Page Copyright Page Table of Contents Preface Authors Chapter 1 Overview 1.1 Introduction 1.2 Towards Intelligent Traffic Flow Prediction 1.3 Broad Factors Impacting Traffic Flow 1.4 Prediction Techniques on Traffic Flow 1.5 Generic Traffic Flow Prediction Models and Measurements 1.6 Motivation 1.7 Problem Statement and Research Objective 1.7.1 Problem Statement 1.7.2 Research Objectives 1.8 Outline of the Book Chapter 2 Related Works 2.1 Introduction 2.2 Traffic Flow Prediction 2.2.1 Parametric Approaches 2.2.2 Nonparametric Approaches 2.3 Traffic Incident Detection 2.4 Smart Traffic Prediction and Congestion Avoidance System 2.4.1 Approaches to Congestion Control 2.5 Short-Term Traffic Prediction Model 2.6 Traffic Light Controller and Deviation System 2.7 IoT-Based Intelligent Transportation Systems 2.8 Summary Chapter 3 Smart Traffic Prediction and Congestion Avoidance System (S-TPCA) Using Genetic Predictive Models for Urban Transportation 3.1 Introduction 3.2 Smart Traffic Prediction 3.3 Congestion Avoidance System 3.4 Preliminaries 3.5 TPCA System 3.5.1 Gathering Traffic Data 3.5.2 Identification of Traffic State 3.5.2.1 Min–Max Normalization 3.5.3 Traffic Prediction and Congestion Avoidance 3.5.4 Rerouting and Fuel Consumption Model 3.6 Experiment Results and Discussion 3.7 Summary Chapter 4 Short-Term Traffic Prediction Model (STTPM) 4.1 Introduction 4.2 Need for Traffic Flow Prediction 4.3 Dataset Collection 4.4 Traffic Flow Analysis 4.5 Short-Term Traffic Flow Prediction 4.5.1 Locally Weighted Learning (LWL) 4.5.2 Traffic Flow Structure Pattern Based Prediction Method 4.6 Experiment Results and Discussion 4.7 Summary Chapter 5 An Efficient Intelligent Traffic Light Control and Deviation System 5.1 Introduction 5.2 An Efficient Intelligent Traffic Light Control and Deviation System 5.2.1 Elements of the Proposed Framework 5.2.1.1 Sensors 5.2.1.2 Data Collector Agent 5.2.1.3 Data Processor Agent 5.2.1.4 Intelligent Traffic Light Controller 5.2.1.5 Intelligent Traffic Deviation System 5.2.2 Vehicle Detection and Counting 5.2.3 Vehicle Categorization 5.2.4 Compute Vehicle Length Depending on Speed 5.2.5 Light Control System and Measurement of Vehicle 5.2.6 Traffic Deviation System 5.3 Results and Discussion 5.3.1 Conversion of Map to Graph 5.3.2 Validation 5.4 Summary Chapter 6 IoT-Based Intelligent Transportation System (IoT-ITS) 6.1 Introduction 6.2 Internet of Things 6.3 Intelligent Transport System 6.4 IoT-Based Intelligent Transport System 6.5 S-ITS System Overview and Preliminaries 6.5.1 Design Requirements of ITS System 6.5.2 Design Goals 6.5.2.1 Scalability 6.5.2.2 Reliability 6.5.2.3 User-Friendliness 6.5.3 Experimental Design 6.5.3.1 Vehicular Location Tracking 6.5.3.2 Intelligent Vehicle Parking System 6.5.3.3 Communication within a VANET 6.5.3.4 Vehicular Big-Data Mining 6.5.4 Implementation 6.5.4.1 Big Data Techniques in ITS 6.5.4.2 Classification of Multivariate Techniques 6.5.4.3 Multiple Regression Analysis 6.5.4.4 Multiple Discriminant Analysis 6.5.4.5 Logistic Regression 6.5.4.6 Conjoint Analysis 6.5.4.7 Cluster Analysis 6.6 Experiment Results and Discussions 6.7 Summary Chapter 7 Intelligent Traffic Light Control and Ambulance Control System 7.1 Introduction 7.2 Intelligent Traffic Light Control System 7.3 Ambulance Control System 7.3.1 Traffic Coordination at Road Intersections 7.4 Intelligent Traffic Light Control with an Ambulance Control System 7.4.1 Prototype Design Specification 7.4.2 Hardware Design and Connections 7.4.3 Compass Sensor Library – ADAFRUIT 7.4.4 Software Design and Coding 7.4.4.1 Traffic Light Control System Module 7.4.4.2 Ambulance Control System Module 7.4.4.3 Codes Download Constraints in Arduino, i.e. Uploading Coding into Arduino Board 7.4.4.4 Device Driver Installation 7.4.4.5 Benefits of Intelligent Traffic Light Control System with Ambulance Control System 7.4.4.6 Limitations of the Intelligent Traffic Light Control System with Ambulance Control System 7.5 Results and Discussion 7.6 Summary Chapter 8 Conclusions and Future Research 8.1 Conclusions 8.2 Scope for Future Research Bibliography Index
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