• Covers core analytical and numerical methods for modeling, planning, operations, and control of transportation systems. Traffic flow theory, vehicle dynamics and behavior, numerical integration and simulation, graphical analysis. Properties of delays, queueing theory. Resource allocation, optimization models, linear and integer programming. Autonomy in transport, Markov Decision Processes, reinforcement learning, deep learning. Applications drawn broadly from land, air, and sea transport; private and public sector; transport of passengers and goods; futuristic, modern, and historical. Hands-on computational labs. Linear algebra background is encouraged but not required. Students taking graduate version complete additional assignments.

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