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This course provides a comprehensive examination of Artificial Intelligence applications in transportation engineering, including traffic flow optimization, intelligent transportation systems (ITS), infrastructure monitoring, predictive maintenance, transportation safety analysis, sustainability planning, and smart mobility integration. Participants explore how AI technologies integrate with transportation sensors, communication networks, geographic information systems (GIS), structural monitoring platforms, and transportation management systems to improve operational efficiency, infrastructure reliability, and mobility performance. Emphasis is placed on the role of AI as a decision-support tool that complements professional engineering expertise while preserving public safety and regulatory compliance.
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Learning Objectives:
Upon completion of this course, participants will be able to:
- Explain fundamental Artificial Intelligence technologies and analytical methods applicable to transportation engineering systems, including machine learning, neural networks, reinforcement learning, and anomaly detection.
- Describe transportation data infrastructure components, sensor technologies, communication networks, and Intelligent Transportation System (ITS) architectures required to support AI-enabled transportation applications.
- Identify opportunities for AI-driven traffic flow modeling, congestion prediction, and adaptive traffic control strategies that improve transportation system efficiency while maintaining safety requirements.
- Evaluate how Artificial Intelligence supports monitoring, predictive maintenance, and lifecycle management of transportation infrastructure such as pavements, bridges, and structural systems.
- Analyze the role of AI in transportation safety engineering, including crash risk prediction, incident detection, driver behavior analysis, and infrastructure hazard identification.
- Explain how AI technologies enhance transportation sustainability, multimodal mobility planning, freight efficiency, and smart city integration while supporting environmental and energy efficiency objectives.
- Recognize cybersecurity risks, ethical responsibilities, regulatory considerations, and professional accountability associated with implementing AI technologies in transportation infrastructure systems.
- Assess emerging transportation technologies, including autonomous vehicles, connected infrastructure, digital twins, and intelligent mobility platforms, and their potential impacts on transportation engineering practice.
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