CE 452 Transportation Systems Simulation and Modeling
Fundamentals of python programming, random numbers, simulation, statistical analysis and models for continuous, count, and discrete data and structural equations using transportation data. Introduction to transportation geospatial data and supervised and unsupervised machine learning. Utilization of large language models (LLMs) for coding and data collection. Applications to transportation systems design.
Slash Listed Courses
Also offered for graduate-level credit as
CE 552 and may be taken only once for credit