CivE 612

Transportation Planning: Methodology and Techniques

Description

The objective of this course is to provide students an in-depth discussion on the fundamental concepts and methodologies applied in the urban transportation planning process. The primary focus of the course is on the development of various models applied in the urban demand forecasting process, including traditional four-step models and activity-based models (ABM). Important methodologies that will be covered in this course include linear (logistic) regression, categorical analysis, random utility maximization (RUM) theory, maximum likelihood (ML) estimation, shortest path algorithm, equilibrium assignment methods.

Learning Outcomes

- Analyze and Apply Demand Forecasting Models: Students will be able to evaluate and develop urban transportation demand forecasting models, demonstrating a comprehensive understanding of both traditional four-step models and modern activity-based models (ABM).
- Utilize Advanced Planning Methodologies: Students will be able to apply key mathematical and statistical methodologies—including linear/logistic regression, categorical analysis, and maximum likelihood (ML) estimation—to solve complex transportation planning problems.
- Implement Network Assignment Algorithms: Students will be able to formulate and execute network routing solutions using the shortest path algorithm and equilibrium assignment methods to simulate real-world urban traffic distribution.

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• Co-Requisites
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Undergraduate Program(s)

Sections & Respective Instructors

A1 - 2026/2027 - Fall - Tae Kwon

A1 - 2025/2026 - Winter - Tae Kwon

A1 - 2024/2025 - Winter - Tae Kwon

A1 - 2023/2024 - Fall - Tae Kwon

A1 - 2022/2023 - Fall -

A1 - 2020/2021 - Fall - Tae Kwon