Predictive Analytics for Infrastructure Maintenance and Traffic Modeling
English | September 26, 2026 | ASIN: B0HL2P5Z8T | 428 pages | Epub | 7.51 MB
Transportation infrastructure is evolving from reactive management toward intelligent, data-driven, and predictive systems. Predictive Analytics for Infrastructure Maintenance and Traffic Modeling explores how Artificial Intelligence, Machine Learning, computer vision, sensor technologies, and predictive analytics can transform the way roads, bridges, traffic networks, and other transportation assets are monitored, analyzed, maintained, and managed.The book brings together two closely connected challenges: predicting infrastructure deterioration and forecasting transportation and traffic behavior. It examines how historical records, real-time sensor data, inspection information, traffic observations, environmental conditions, connected vehicles, and visual data can be integrated to generate actionable transportation intelligence. Readers will explore the foundations of modern Intelligent Transportation Systems (ITS), infrastructure condition assessment, deterioration mechanisms, statistical and probabilistic prediction models, machine learning, deep learning, predictive maintenance, traffic-flow theory, short-term traffic forecasting, anomaly detection, computer vision, and integrated infrastructure and traffic modeling.The book also addresses the practical architecture required to transform raw transportation data into engineering and operational decisions. It discusses sensing technologies, communication networks, edge and cloud computing, data platforms, AI analytics, decision-support systems, and continuous feedback mechanisms that enable transportation systems to move from observe and react toward predict and act.Key areas covered include:AI-driven Intelligent Transportation Systems and their architectural foundationsInfrastructure deterioration mechanisms and condition assessmentStatistical and probabilistic models for infrastructure predictionMachine learning and deep learning for predictive maintenanceInfrastructure asset management and remaining useful life estimationTraffic-flow theory, traffic forecasting, and congestion predictionAdvanced traffic modeling and anomaly detectionComputer vision for vehicle recognition and infrastructure inspectionSensor-based transportation data acquisition and analytics architecturesIntegrated infrastructure and traffic modelingCybersecurity and governance of intelligent infrastructureSmart-city applications and real-world infrastructure deploymentsAdvanced predictive analytics and future autonomous transportation systemsParticular attention is given to the practical challenges of deploying predictive systems in real operational environments, including incomplete and noisy data, changing traffic conditions, model uncertainty, infrastructure variability, cybersecurity, and the need for human engineering judgment.Written for researchers, civil and transportation engineers, infrastructure professionals, technology practitioners, students, and decision-makers, this book provides a comprehensive perspective on how predictive analytics can support proactive maintenance, intelligent traffic management, infrastructure planning, and the development of smarter and more resilient transportation systems.ISBN: 978-93-49929-15-9ScienceTech Xplore
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