My last Book (in Persian), published by Amirkabir University of Technology Press:

Decision Support and Expert Systems in Intelligent Transportation Systems

By: Mehdi Ghatee
Abstract:
Intelligent transportation systems are important parts of our life and provide various services such as data collection and processing, traffic management, travel information, public transit, freight transportation, and rescue management. These services lead to safety, security, low congestion, economic productivity, fuel saving, and efficiency. Intelligent transportation systems are incorporated with the systems based on information technology, wireless communication, smartphone, IoT and UAV to solve transportation problems. This book has involved with this field by focusing on decision support and expert systems. DSS supports decision-making processes, and expert systems can be applied for decision making in the near future. In this book, first, the basic concepts of intelligent transportation are presented. Then, the principles of decision-making and expert systems are examined. In the following, transportation problems are divided into general structured models, advanced structured models and non-structured models. Then, based on the outlined frameworks, a number of advanced transportation systems and smart city systems are explained. The last two chapters will cover decision-making and expert systems in autonomous cars and smart roads. The study of this book is recommended to who is interested in interdisciplinary studies of transportation.

 

Chapter One: Introduction and Framework of Book
    1-1   Intelligent Transportation System Services
    1-2   Intelligent transportation system requirements
    1-3   Intelligent components in transportation
    1-4   Intelligent transportation systems architecture
    1-5   The need for smart decision making in transportation
    1-6   Decision Making Systems
    1-7   Expert Systems
    1-8   Summary of Chapter
    1-9   Exercises and Projects of Chapter
Chapter Two: Intelligent Transportation Systems
    2-1 Intelligent Transportation Architecture Pyramid
    2-2 Traffic Management Subsystem
    2-3 Travel Information Subsystem
    2-4 Commercial Vehicle Operations Subsystem
    2-5 Advanced Public Transportation Subsystem
    2-6 Car Safety Subsystem
    2-7 Emergency Management Subsystem
    2-8 Construction, maintenance and repair management subsystem
    2-9 Data Archive Management Subsystem
    2-10 Advanced Human-based Transportation Subsystem
    2-11 Summary of Chapter
    2-12 Exercises and Projects of Chapter
Chapter 3: Principles and Structures of DSS
    3-1 Decision, decision support and expertise
    3-2 Decision making process
    3-3 Decision frameworks and tools
    3-3-1 Structure of decision models
    3-4 Uncertainty in decision
    3-5 Identify decision-making tools
    3-6 Evaluation after decision
    3-7 Dimensions of transport decision making
    3-8 Management Information Systems
    3-9 Decision Systems
    3-9-1 Types of approaches in the development of decision-making systems
    3-9-2 Types of decision-making systems based on input and output
    3-9-3 Factors influencing the definition of decision-making system
    3-9-4 Architectural components of a DSS
    1-4-9-3 Database
    2-4-9-3 Model Base
    3-4-9-3 Knowledge Base
    4-4-9-3 User Interface
    10-3 Summary of Chapter
    11-3 Exercises and Projects of Chapter
Chapter 4: Principles and Structures of Expert Systems
    4-1 Supervised Learning
    4-1-1 Decision Tree
    4-1-2 New Bay Tree
    4-1-3 Artificial Neural Networks
    4-2 Unsupervised Learning
    4-2-1 k-Mean Algorithm
    4-2-2 Self-organized mapping
    4-2-3 Hierarchical clustering
    4-3 Reinforcement Learning
    4-4 Neural trees and knowledge transfer
    4-5 Summary of Chapter
    4-6 Exercises and Projects of Chapter   
Chapter 5: General Structured Models in Transportation
    5-1 Linear Optimization Models
    5-2 Nonlinear Optimization Models
    5-3 Integer Optimization Models
    5-4 Network Optimization Models
    5-4-1 Problem of Location of Single Facility
    5-4-2 Maximum Flow Problem
    5-4-3 Assignment Problem
    5-4-4 Minimum Cost Flow Problem
    5-4-5 Circulation Problem
    5-4-6 Generalized Networks
    5-4-7 Multicommodity Flow Problem
    5-4-8 Shortest Path Problem
    5-4-9 Route Guidance Problem
    5-4-10 Similar Path Problem
    5-4-11 Critical Path of the Project
    5-4-12 Investment Problem
    5-4-13 Hamiltonian Paths
    5-4-14 Crew Scheduling Problem
    5-5 Practical Example in Intelligent Transportation
    5-6 Summary of Chapter
    5-7 Exercises and Projects of Chapter
Chapter Six: General Structured Models in Transportation
    6-1 Traffic Assignment Models
    6-1-1 Probit Model
    6-1-2 Logit Model
    6-2 Fuzzy Traffic Assignment
    6-3 Path Enumeration Problem
    6-3-1 Strategies of Different Labels
    6-3-2 Column Generation Strategy
    6-3-3 K-Shortest Path Problem
    6-3-4 Removal and Penalty Definition
    6-3-5 K-Similar Paths
    6-3-6 Stochastic Shortest Path Problem
    6-3-7 Fuzzy Shortest Path Problem
    6-4 Hazmat Transportation
    6-5 Network Control Problem
    6-5-1 Genetic Algorithm for Signal Optimization
    6-5-2 Using Metamodels
    6-6 Transportation Network Design Problem
    6-6-1 Mathematical Models of Network Design
    6-6-2 Advanced Multicommodity Flow Problem
    6-6-3 Lagrangian Relaxation for Network Design
    6-6-4 Bi-level Programming for Network Design
    6-6-5 Benders Decomposition in Network Design
    6-6-6  Network Design Problem
    6-7 Transportation Infrastructure Management
    6-8 Summary of Chapter
    6-9 Exercises and Projects of Chapter
Chapter 7: Non-structured Models in Transportation
    7-1 Scenario-based decision making models
    7-2 Rule-based decision-making models
    7-3 Machine Learning Decision Making Models
    7-4 Combined decision making models
    7-5 Summary of Chapter
    7-6 Exercises and Projects of Chapter
Chapter 8: Advanced Systems in Intelligent Transportation
    8-1 Systems based on Grids and Clouds
    8-1-1 Grid Computing in Intelligent Transportation System
    8-1-2 Cloud Computing for Intelligent Transportation System
    8-2 GPU-based Systems
    8-3 Systems based on Big Data Processing
    8-4 Deep Learning Based Systems
    8-5 Image Processing based Systems
    8-6 UAV based Systems
    8-7 Systems based on Connected Vehicles
    8-8 Telecommunication-based Data Collection Systems
    8-9 Smartphone Based Driving Evaluation Systems
    8-10 Context-Aware Mobile Based Systems
    8-11 Autonomous Vehicles
    8-12 Summary of Chapter
    8-13 Exercises and Projects of Chapter
Chapter 9: Decision Making Systems in Smart Cars
    9-1 Decision Systems for Passenger Safety
    9-2 Car Lighting Systems
    9-3 Car Braking Systems
    9-4 Collision Warning Systems
    9-5 Adaptable Speed Systems
    9-6 Line Control Systems
    9-7 Driver Assistant Systems
    9-8 Systems for Light Vehicles
    9-9 Systems Related to Heavy Vehicles
    9-9-1 Public Transportation Management System
    9-9-2 Cargo Transportation Management System
    9-10 Summary of Chapter
    9-11 Exercises and Projects of Chapter
Chapter 10: Decision Making Systems in Smart Smart Roads
    10-1 Services Related to Control Centers
    10-1-1 Traffic Management
    10-1-2 Transportation Data Archive Management
    10-1-3 System for Incident Detection
    10-2 Node Services
    10-2-1 Adaptive Traffic Light Control
    10-2-2 Stop Management
    10-2-3 Smart Bridges
    10-3 Arteries and Highways Services
    10-4 Electronic Payment Services
    10-5 Vehicle to Infrastructure Services
    10-6 Intelligent Transportation Architecture Design
    10-7 Summary of Chapter
    10-8 Exercises and Projects of Chapter