CET 581: Transportation Demand Forecasting. T/TH 10:00-11:20 am.

CET 581 focuses on equipping students with the knowledge and skills to build travel demand forecasting models for both long- and short-term planning. Travel demand forecasting models predict the amount of trips people make, where and when they go, what modes of transportation they will take, and what route they will choose.  The topics covered will include both traditional methods ranging from cross-classification, to linear regression and discrete choice models, and to machine learning methods such as support vector machines. Students will complete multiple, guided projects via Jupyter Notebooks and a term project.

Prerequisites include prior knowledge of probability and statistics, matrix algebra, and calculus; prior programming experience (this course will use Python). This class is open to both graduate students and senior-level undergraduates.

Learning objectives include an understanding of transportation behavior and demand forecasting; learning analytical models relevant to transport planning; and working with real-world transportation datasets using Python and Jupyter Notebooks.

Please contact the instructor, Kaitlyn Ng (kng9@uw.edu), if you have any questions.

Cynthia Chen, PhD

Professor and Interim Chair, Department of Industrial & Systems Engineering

Professor, Department of Civil and Environmental Engineering

University of Washington
Seattle, WA 98195
Office: 206-543-8974
Email: qzchen@uw.edu
THINKLAB:  https://sites.uw.edu/thinklab/

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