Advanced Intelligent Predictive Models for Urban Transportation

✍ Scribed by R Sathiyaraj, A Bharathi, B Balamurugan

• Publisher: Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 145
• Category: Library

✦ Synopsis

<p>The book emphasizes the predictive models of Big Data, Genetic Algorithm and IoT with a case study.&nbsp;It also covers an Intelligent Traffic Light Controller and Deviation System, which can assist in modernizing the traffic system to smart traffic management system.</p>

✦ Table of Contents

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