Students in this course will learn the data-driven methods used by transportation planners to explore, visualize, quantify, and predict transportation and travel demand. The content of the course is divided into three parts. The first introduces the concepts and methods to analyze urban travel behavior. We cover data sources, visualization, and analytics, followed by theory and methods of transportation demand modeling. Our study of the four step model will include statistical methods to forecast trip generation and distribution, modal split, and route assignment. We spend additional time on the econometric models of behavior and modal choice. The second part of the course examines the tools transportation planners and engineers use in predicting travel behavior and making rational recommendations among transportation alternatives. We will do hands-on labs with a transportation demand modeling software package commonly used in planning: VISUM, the standard, widely-used travel demand modeling platform. Our hands-on labs are designed to test the impact of transportation improvements and changes on travel patterns in both fictional and real-world case studies. Through the labs, students will learn how to predict travel patterns and compare among potential transportation investments and policies, a critical component of transportation and infrastructure planning. The third and final part of the course looks at emerging trends in transportation methods and analysis, critiques of current practice, and case studies of transportation demand modeling in scheduled urban transit systems.