Artificial Intelligence in Highway Safety 1st Edition by Subasish Das ISBN 9780367436704 0367436701

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Product details:

ISBN 10: 0367436701
ISBN 13: 9780367436704
Author: Subasish Das

Artificial Intelligence in Highway Safety 1st Edition Table of contents:

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 Parameters and 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

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

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