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Full Description
The transportation industry faces unprecedented challenges as systems grow increasingly complex. This book introduces an integrated framework for transport accident investigations by combining traditional human performance analysis with advanced artificial intelligence capabilities. Moving beyond simplistic cause-and-effect models, the authors cover human factors, automation, hardware components, contextual variables, and organizational elements that collectively determine safety outcomes in complex socio-technical environments.
Building upon established methodologies including HFACS, STAMP, and AcciMap, this title analyzes AI-enabled approaches including digital twins, weak signal detection, pattern recognition, and data fusion platforms. Each chapter delivers to-the-point practical insights into dynamic modeling of systemic interactions and enhanced detection of failure precursors. The innovative MAHCO model, combined with AI analytics, supports transparent decision-making and institutional learning while creating intelligent safety ecosystems that transform how we understand and prevent accidents in transportation systems. Readers will develop an effective introduction to these systems and their capabilities thus honing their foundational knowledge.
Investigating Transport Accidents: Human Performance and Intelligent Systems is for engineers, policy-makers, researchers and students studying for or already working in transportation safety. It will also appeal to pilots, controllers, maintainers, and trainers in the aerospace industry.
Contents
1. Rethinking Human Performance in High-Reliability Transportation Systems. 2. Theoretical Foundations of Human Performance. 3. Investigative Methodologies and Tools. 4. AI Technology in Investigation: AI, Digital Twins, and Data Fusion. 5. Human Performance Traps: Technological, Organizational, Regulatory, and Cultural Challanges. 6. The Human Operator: Health, Cognitive Resilience, Workload, and Performance. 7. Team Dynamics and Communication in Incidents-Accidents Management. 8. Maintenance and Inspection in a Digitally Augmented Era. 9. Data Sources in Modern Investigations. 10. Toward a New Paradigm: Human-Centric, AI-Supported Safety Ecosystems.
