Artificial Intelligence in Highway Safety

✍ Scribed by Subasish Das

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 354
• Edition: 1
• Category: Library

✦ Synopsis

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend.

Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

✦ Table of Contents

Cover
Title Page
Copyright Page
Dedication
Preface
Table of Contents
List of Abbreviations
1. Introduction
1.1. Highway Safety
1.2. Artificial Intelligence
1.2.1. Idea of Artificial Intelligence
1.2.2. History of AI
1.2.3. Statistical Model vs. AI Algorithm: Two Cultures
1.3. Application of Artificial Intelligence in Highway Safety
1.4. Book Organization
2. Highway Safety Basics
2.1. Introduction
2.2. Influential Factors in Highway Safety
2.3. 4E Approach
2.3.1. Engineering
2.3.2. Education
2.3.3. Enforcement
2.3.4. Emergency
2.4. Intervention Tools
2.5. Data Sources
2.6. Crash Frequency Models
2.7. Crash Severity Models
2.8. Effectiveness of Countermeasures
2.8.1. Observational B/A Studies
2.9. Benefit Cost Analysis
2.10. Transportation Safety Planning
2.11. Workforce Development and Core Competencies
2.11.1. Occupational Descriptors
2.11.2. Core Competencies
3. Artificial Intelligence Basics
3.1. Introduction
3.2. Machine Learning
3.2.1. Supervised Learning
3.2.2. Unsupervised Learning
3.2.3. Semi-supervised Learning
3.2.4. Reinforcement Learning
3.2.5. Deep Learning
3.3. Regression and Classification
3.3.1. Regression
3.3.2. Classification
3.4. Sampling
3.4.1. Probability Sampling
3.4.2. Non-probability Sampling
3.4.3. Population Parameters and Sampling Statistics
3.4.4. Sample Size
4. Matrix Algebra and Probability
4.1. Introduction
4.2. Matrix Algebra
4.2.1. Matrix Multiplication
4.2.2. Linear Dependence and Rank of a Matrix
4.2.3. Matrix Inversion (Division)
4.2.4. Eigenvalues and Eigenvectors
4.2.5. Useful Matrices and Properties of Matrices
4.2.6. Matrix Algebra and Random Variables
4.3. Probability
4.3.1. Probability, Conditional Probability, and Statistical Independence
4.3.2. Estimating Parameters in Statistical Models
4.3.3. Useful Probability Distributions
4.3.4. Mean, Variance and Covariance
5. Supervised Learning
5.1. Introduction
5.2. Popular Models and Algorithms
5.2.1. Logistic Regression
5.2.2. Decision Tree
5.2.3. Support Vector Machine
5.2.4. Random Forests (RF)
5.2.5. Naïve Bayes Classifier
5.2.6. Artificial Neural Networks
5.2.7. Cubist
5.2.8. Extreme Gradient Boosting (XGBoost)
5.2.9. Categorical Boosting (CatBoost)
5.3. Supervised Learning based Highway Safety Studies
6. Unsupervised Learning
6.1. Introduction
6.2. Popular Algorithms
6.2.1. K-Means
6.2.2. K-Nearest Neighbors
6.3. Dimension Reduction Methods in Highway Safety
6.4. Categorical Data Analysis
6.4.1. The Singular Value Decomposition
6.5. Correspondence Analysis
6.5.1. Multiple Correspondence Analysis
6.5.2. Taxicab Correspondence Analysis
6.6. Unsupervised Learning, Semi-Supervised, and Reinforcement Learning based Highway safety Studies
7. Deep Learning
7.1. Introduction
7.2. Popular Algorithms
7.2.1. LSTM
7.2.2. Monte Carlo Sampling
7.3. Boltzmann Machines
7.3.1. Boltzmann Machine Learning
7.3.2. Generative Adversarial Networks
7.4. Deep Learning Categories
7.4.1. Convolutional Neural Networks (CNNs)
7.4.2. CNN Structure
7.4.3. CNN Architectures and Applications
7.4.4. Forward and Backward Propagation
7.4.5. Pretrained Unsupervised Networks
7.4.6. Autoencoders
7.4.7. Deep Belief Network
7.4.8. Recurrent and Recursive Neural Networks
7.5. Deep Learning based Highway Safety Studies
8. Natural Language Processing
8.1. Introduction
8.2. Text Mining
8.3. Topic Modeling
8.3.1. Latent Dirichlet Allocation
8.3.2. Structural Topic Model (STM)
8.3.3. Keyword Assisted Topic Model
8.3.4. Text Summarization
8.4. Sentence Centrality and Centroid-based Summarization
8.5. Centrality-based Sentence Salience
8.5.1. Eigenvector Centrality and LexRank
8.5.2. Continuous LexRank
8.6. NLP Based Highway Safety Studies
9. Explainable AI
9.1. Introduction
9.1.1. Partial Dependence Plot (PDP)
9.1.2. Individual Conditional Expectation (ICE)
9.1.3. Accumulated Local Effects (ALE) Plot
9.1.4. Local Surrogate (LIME)
9.1.5. Shapley Value
9.1.6. SHAP (SHapley Additive exPlanations)
10. Disruptive and Emerging Technologies in Highway Safety
10.1. Introduction
10.2. Risks Associated with Emerging and Disruptive Technologies
10.2.1. Connected and Autonomous Vehicles
10.2.2. Electric Vehicles
10.2.3. Mobility as a Service/Mobility on Demand
10.2.4. Advanced Air Mobility
10.3. Studies on Emerging and Disruptive Technologies
11. Conclusions and Future Needs
11.1. Introduction
11.2. Highway Safety AI 101
11.3. Ethics in Highway Safety AI
11.3.1. Ethics and Regulation
11.3.2. Bias, Fairness, Interpretability, Robustness, and Security
11.3.3. Governance
11.4. AI based Highway Safety Guidances
Appendix A: Case Study of Exploratory Data Analysis
Appendix B: Steps of Big Data Analysis in Highway Safety
Appendix C: ML Interpretability and Model Selection
Appendix D: Develop an Interactive Map
Appendix E: Develop an interactive Shiny App for Highway Safety Analysis with AI Models
Appendix F: Develop an Interactive Shiny App with Application Programming Interface (API) based Queries
Appendix G: Alternative to Crash Tree Tool
Appendix H: Example of Quick Bibliographic Search
Appendix I: Example of Self-Organizing Maps
Appendix J: Example of Correspondence Analysis
Appendix K: Example of Deep Explainer
Appendix L: Road Safety Professional (RSP) Certification Needs
Index