CIVE 7381 — Transportation Demand Forecasting and Model Estimation
4 semester hoursGraduateLectureusually offered: falltypical days: M/WBostonOnlineTraditionalVideo Streaming
Studies methods used for model estimation, model building, and interpretation of results. Emphasizes travel demand forecasting, including trip generation, distribution, model choice, and route choice. Topics include aggregate and disaggregate models, including discrete choice (binary and multinomial logit and extensions), model building and statistical testing, aggregation, sampling, and sample design. Demonstrates the applicability and underlying principles of the various models through case studies with focus on practical aspects and interpretation. Bases main methodological approaches on econometric methods, mainly on regression modeling and maximum likelihood estimation. Uses general and specialized software tools for data analysis and model estimation. While the focus is on estimating transportation demand models, the methods are applicable to a broad class of applications in engineering, marketing, etc.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 2 | 7 | 30 | 23% | 11.5 |
| Fall 2024 | 2 | 13 | 30 | 43% | 8.5 |
| Fall 2025 | 1 | 4 | 15 | 27% | 11.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 79% · T 0% · W 79% · Th 0% · F 0%
Common patterns: MW (79% of sections), async (21% of sections) — in patterns, R means Thursday
Professors
Fall
- Harilaos Koutsopoulos (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (CIVE course descriptions) · Student reviews on RateMyHusky · All CIVE courses · Plan it at numap.app