Artificial Intelligence and Human Performance in Transportation: Applications, Challenges, and Future Directions

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Foreword
Acknowledgments
About the Editors
Contributors
Introduction
Chapter 1 Artificial Intelligence in Aviation
   1.1 Conceptual Framework for Artificial Intelligence in Transportation
   References
   1.2 Evolution of AI and Its Potential Applications
   References
   1.3 Benefits of AI Implementation in Transportation
      1.3.1 AI on Transforming Transportation
   References
   1.4 Control Ergonomics in the Era of AI
   References
   1.5 Learning to Trust AI and ML – Key Steps for Implementing Changes
   References
   1.6 AI Certification & Challenges
      1.6.1 Why Should We Regulate?
      1.6.2 AI Regulatory Approaches
      1.6.3 EU Aviation AI Blended Approach
      1.6.4 Conclusions
   References
   1.7 Simulated ATC Environment (SATCE) and ASTi SERA’s Role in CBTA: The Purdue University Case Study
   Reference
Chapter 2 Artificial Intelligence Training and Operations
   2.1 Incorporating AI into Transportation Higher Education: Curriculum Considerations for an Evolving Industry
      2.1.1 Contemporary Examples of Artificial Intelligence Being Integrated into Higher Education
      2.1.2 Summary
      References
   2.2 The Role of AI in AI Training/Operations
      2.2.1 Mental Rehearsal in Aviation and Emerging Technologies: The Step Forward in Training
      2.2.2 Benefits of Extended Reality (XR) in Pilots’ Mental Rehearsal
      2.2.3 Artificial Intelligence in Shipping Operations
   References
   2.3 The Role of AI in Weather Prediction, Planning, Route Optimization, and Scheduling
      2.3.1 AI in Weather Prediction
      2.3.2 AI in Planning and Decision Making
      2.3.3 Route Optimization
      2.3.4 Scheduling Adjustments
      2.3.5 AI in Weather Prediction, Planning, Route Optimization, and Scheduling: The Shipping Case Study
   References
   2.4 AI-Powered Maintenance and Predictive Analysis for Vehicle Health Monitoring and Maintenance Scheduling
      2.4.1 Big Data Analytics in Aviation Maintenance
      2.4.2 Machine Learning for Failure Prognostics
      2.4.3 Integrated Frameworks and Data-Driven Techniques for Predictive Maintenance
      2.4.4 Digital Twins
   References
   2.5 Artificial Intelligence Applications as Cognitive Artifacts in Aviation Distributed Cognition
   References
   2.6 The VTR Case Study
      2.6.1 The Genesis of Visionary Training Resources (VTR)
      2.6.2 VTR Products Show Positive Results in Proof-of-Concept (POC)
      2.6.3 Incorporating VR into Pilot Training
      2.6.4 Generating and Using VR Data
      2.6.5 The Future of Virtual Reality Airline Training
Chapter 3 Artificial Intelligence in Traffic Management
   3.1 Applications of AI in Traffic Management and its Challenges
      3.1.1 Individual Vehicles – Challenges and Benefits
      3.1.2 Network and Traffic Management
      3.1.3 Merging of Two Directions
   References
   3.2 Traffic Prediction and Optimization
      3.2.1 Traffic Optimization
      3.2.2 Future Directions in AI for Aviation Traffic Management
      3.2.3 Safety and Security
      3.2.4 Technological Challenges in AI for Aviation Traffic Management
   References
   3.3 Role of AI in Unmanned Aircraft Systems and the Drone Traffic Management System
      3.3.1 Augmenting the Envelope: Machine Assistance to Remote Aviation (MARA)
      3.3.2 What Is Machine Assistance?
      3.3.3 MARA and EASA
      3.3.4 Future Research
      3.3.5 Conclusion
   References
   3.4 Role of AI in Unmanned Aircraft Systems and the Drone Traffic Management System
      3.4.1 The World below 400 Feet Above Ground Level
      3.4.2 Towards Control in the Uncontrolled
      3.4.3 MARA Monitoring
      3.4.4 Conclusions
   References
   3.5 The Role of AI in Training of Traffic Controllers – Managers
      3.5.1 Overview of the Typical ATCO Training Cycle
      3.5.2 Where AI Can Be Used to Improve and Speed Up Training
      3.5.3 Conclusions
   References
   3.6 Monitoring Human Performance, Safety Intelligence, and Fatigue Risk Management in Traffic Ecosystems
      3.6.1 Fatigue Risk and Workload Management Applications of Artificial Intelligence
   References
   3.7 Safety Assurance, Safety Management, and the Design of Airspace/Traffic Management
      3.7.1 Human Performance and Fatigue as Examples of Weak Signal Analysis
      3.7.2 Why AI Will Take This and Make It Better, but with Challenges
      3.7.3 How Does Safety Intelligence Feed Safety Assurance?
      3.7.4 Next Steps Towards Parameterization
   References
   3.8 The Austro Control Case Study
      3.8.1 The Components and Master Supervisory Decision-Support System
      3.8.2 Evolving to an AI Driven System with Humans in the Loop
      3.8.3 Conclusions and Building Trust
Chapter 4 Artificial Intelligence in Airport – Ports – Train Stations Operations
   4.1 Applications of AI in Airports – Ports – Train Stations Management, Security, and Surveillance Systems
      4.1.1 Introduction
      4.1.2 Airports
      4.1.3 Ports
      4.1.4 Train Stations
      4.1.5 The Shipping Companies Case Study
   References
   4.2 The Use of AI in Baggage Handling, Passenger Screening, and Facial Recognition within Airport, Port, and Train Station Operations
      4.2.1 AI in Passenger Screening
      4.2.2 AI in Facial Recognition
      4.2.3 Integration Challenges and Solutions
   References
   4.3 The Role of AI in Multiple Remote Towers and Ground Operations
      4.3.1 AI Solutions for Multiple Remote Tower Operations
      4.3.2 AI Solutions for MRTO Challenges
      4.3.3 Conclusion
   References
   4.4 Artificial Intelligence in Shipping Operations
      4.4.1 Future Options
   References
   4.5 Train Operations
      4.5.1 Applications of AI in Train Operations
      4.5.2 Challenges in Implementing AI in Train Operations
      4.5.3 Outlook and Conclusions
   References
Chapter 5 Artificial Intelligence in Customers’ Experience
   5.1 AI-Based Chatbots and Virtual Assistants for Personalized Customer Interactions
      5.1.1 Evolution of Chatbots and Virtual Assistants
      5.1.2 Technologies Behind AI Chatbots and Virtual Assistants
      5.1.3 Applications in Customer Service
      5.1.4 Benefits for Businesses and the Aviation Industry
      5.1.5 Challenges and Considerations
      5.1.6 Future Trends
   References
   5.2 Use of AI Recommendation Systems, Pricing Optimization, and Revenue Management
      5.2.1 Introduction
      5.2.2 Opportunities for Traditional RM
      5.2.3 Optimizing Add-On Pricing and Sales
      5.2.4 Using AI for Sales of Commission-Based Products and Advertising
      5.2.5 Getting more Out of FFPs
   Reference
   5.3 The Role of AI in Customer Feedback Analysis, Sentiment Analysis, and Social Media Monitoring
      5.3.1 Customer Feedback Analysis
      5.3.2 Sentiment Analysis
      5.3.3 Social Media Monitoring
      5.3.4 Conclusion
   References
   5.4 The Airliners’ Case Study
      5.4.1 Background
      5.4.2 Technical Integration with Legacy Systems
      5.4.3 Outcomes
   Reference
Chapter 6 Artificial Intelligence in Maintenance, Technical Support and Repair Operations
   6.1 Role of AI in Predictive Maintenance and Condition-Based (CB) Monitoring for Vehicle Systems
      6.1.1 Incorporating AI into Aircraft and Maintenance – Documentation and Sensor Data
      6.1.2 Supply Chain Management
      6.1.3 Conclusion
   References
   6.2 Use of AI in Fault Detection, Diagnosis, and Prognosis for Improved Maintenance Efficiency
      6.2.1 Fault Detection
      6.2.2 Fault Diagnosis
      6.2.3 Fault Prognosis – Predicting Failure and Prescribing Fixes
   References
   6.3 AI Applications in Repair Operations and Spare Parts Management
      6.3.1 AI in the Aviation Industry (Case Study)
      6.3.2 AI in Rail Transportation (Case Study)
      6.3.3 AI in the Shipping Industry (Case Study)
   References
   6.4 AI in Maintenance and Supply Chain
   References
   6.5 AI Uses for Technical Support in Transportation Management - Monitoring of Systems: Automation of the Radio PIREP Submission Process in General Aviation (GA)
   References
   6.6 Data Governance: The Basis for AI in Maintenance and Repair Operations
      6.6.1 AI and MRO Business Metrics Considerations
      6.6.2 Predictive Maintenance and Its Tools in MRO
   References
   6.7 The Purdue University School of Aviation and Transportation Technology (SATT) Case Study
      6.7.1 Digital Twin Tools in MRO
   Reference
Chapter 7 Human-Machine Interaction in Artificial Intelligence in Aviation
   7.1 Human Factor Considerations in the Implementation of AI in Transportation
      7.1.1 Key Human Factor Considerations
      7.1.2 Challenges and Opportunities
      7.1.3 Case Study
   References
   7.2 Training and Skill Development for Human Operators Working with AI Systems
      7.2.1 Understanding AI and Its Role in Transportation
      7.2.2 Core Skills for Human Operators
   References
      7.2.3 Prompt Engineering in Generative AI Applications for Transportation: An Emerging Competency
   References
   7.3 The Future of Simulation in HMI in AI in Transportation
      7.3.1 Implications for HMI
      7.3.2 Future Trends
   References
   7.4 Automation Framework for Human Decision-Making and Key Uses of AI for Cognitive Information Processing
      7.4.1 Automation in Decision Making
      7.4.2 Types of Decision Automation
   References
   7.5 Adaptive Automation and AI in Predictive Human Performance
      7.5.1 Adaptive Automation Framework
   References
   7.6 Human-AI Teaming by Implementing AI-Based Applications on Connected Electronic Transportation Devices
      7.6.1 Trajectory-Based Operations
      7.6.2 Air Traffic Control Loop
      7.6.3 Operation Considerations
   References
   7.7 The Electronic Flight Bag Case Study: Exploring the Potential of Machine Learning in 4D Operations
Chapter 8 Ethical and Legal Challenges in the Implementation of Artificial Intelligence
   8.1 Ethical Considerations in the Responsible Implementation of AI in Transportation
      8.1.1 AI Ethics in Aviation
   References
      8.1.2 Ethical and Legal Considerations in Responsible AI Implementation in Shipping
   References
   8.2 Legal Frameworks and Considerations in the Responsible Implementation of AI in Transportation
      8.2.1 Autonomous Vehicles (AVS)
      8.2.2 Data Protection and Privacy
      8.2.3 Regulatory Compliance and Standardization
      8.2.4 Conclusion
   References
   8.3 Building Better Just Culture in a World with AI
   8.4 Safety Management Systems Case Study
      8.4.1 Case Study Overview
   Reference
Chapter 9 Best Practices for the Implementation of Artificial Intelligence
   9.1 Common Challenges and Limitations of Implementing AI in Transportation
   References
   9.2 Best Practices for Successful Implementation of AI in Transportation, including Risk Assessment, Change Management, and Collaboration
      9.2.1 Introduction
      9.2.2 Automation as the Concept of Operations
      9.2.3 The Levels of Automation
      9.2.4 Conclusions
   References
   9.3 Similarities and Differences between the Application of AI in Automotive and Aviation Industries
   References
   9.4 Implementation of Requirements Systems – Start Now or Miss the Chance
Chapter 10 Future Directions and Emerging Trends in Artificial Intelligence in Transportation
   10.1 Future Directions in AI Technologies
   Reference
   10.2 Emerging Trends in AI Technologies
   Reference
   10.3 Predictions and Prospects for the Future of AI in Transportation
   Reference
   10.4 Big Data and Safety Intelligence Possibilities
   Reference
   10.5 The Advanced Air Mobility Case Study: Regulatory Hurdles and Compliance Challenges
   10.6 Conclusions
Index