CE 5560: Transportation Data Analysis
Credits: 3. Contact Hours: Lecture 3.
Statistical, econometric, and data science principles applied to real-world transportation data. Includes identification of data sources and limitations. Fundamentals of reproducibility and replicability, validation (including spatial and temporal validation), differences and purposes of inferential, descriptive, predictive, causal models, etc. Linear regression, count regression, and discrete choice models. Basic utility theory and decision making with applications in transportation. Emphasis is placed on practical applications, proper model development, assumption checking, and usability of results.