Explainable Artificial Intelligence for Intelligent Transportation Systems

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Contributors
SECTION I: Toward Explainable ITS
	CHAPTER 1: Explainable Artificial Intelligence for Intelligent Transportation Systems: Are We There Yet?
SECTION II: Interpretable Methods for ITS Applications
	CHAPTER 2: Towards Safe, Explainable, and Regulated Autonomous Driving
	CHAPTER 3: Explainable Machine Learning Method for Predicting Road-Traffic Accident Injury Severity in Addis Ababa City Based on a New Graph Feature Selection Technique
	CHAPTER 4: COVID-19 Pandemic Effects on Traffic Crash Patterns and Injuries in Barcelona, Spain: An Interpretable Approach
	CHAPTER 5: Advances in Explainable Reinforcement Learning: An Intelligent Transportation Systems Perspective
	CHAPTER 6: Road-Traffic Data Collection: Handling Missing Data
	CHAPTER 7: Explainability of Surrogate Models for Traffic Signal Control
	CHAPTER 8: Intelligent Techniques and Explainable Artificial Intelligence for Vessel Traffic Service: A Survey
	CHAPTER 9: An Explainable Model for Detection and Recognition of Traffic Road Signs
	CHAPTER 10: An Interpretable Detection of Transportation Mode Considering GPS, Spatial, and Contextual Data Based on Ensemble Machine Learning
	CHAPTER 11: Blockchain and Explainable AI for Trustworthy Autonomous Vehicles
SECTION III: Ethical, Social, and Legal Implications of XAI in ITS
	CHAPTER 12: Ethical Decision-Making under Different Perspective-Taking Scenarios and Demographic Characteristics: The Case of Autonomous Vehicles
Index