Transportation Operations
Transportation Operations covers assessment tools, flow theory, flow control, observation/measurement, and scheduled transportation across modes, emphasizing logic over recipes.
Summary
Recent recorded grades — Fall 2023: 3.93 GPA, 100.0% A/AB (n=14 letter grades); Fall 2024: 3.62 GPA, 75.0% A/AB (n=8 letter grades); Fall 2025: 3.75 GPA, 83.3% A/AB (n=6 letter grades).
What you’ll be able to do
Analyze operational characteristics of transportation systems using graphical tools (time-space diagram and cumulative curve)
Analyze vehicular traffic flow using advanced traffic models in different analysis scales: microscopic (vehicle-level), macroscopic (aggregate-level), and network-level scales
Characterize uncertainty in transportation systems
Analyze transportation data based on probability and statistics theories
Design traffic control strategies considering various uncertainties in traffic flow
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapCIV ENGR 370 and (STAT 311, 324, 340, MATH 409, 431,I SY E 210, orE C E 331), graduate/professional standing, or member of Engineering Guest Students
- CIVENGR 370
- take one
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- graduate/professional standing
- member of Engineering Guest Students
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2023: 3.93 GPA, 100.0% A/AB (n=14 letter grades); Fall 2024: 3.62 GPA, 75.0% A/AB (n=8 letter grades); Fall 2025: 3.75 GPA, 83.3% A/AB (n=6 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Flow theory, flow control, observation and measurement techniques, and scheduled transportation.
- Graphical tools for analyzing operational characteristics, including time-space diagrams and cumulative curves.
- Characterizing uncertainty in transportation systems.
Skills
- Assessment tools for transportation operations including flow theory, flow control, observation and measurement techniques, and scheduled transportation.
- Analysis of vehicular traffic flow using advanced traffic models at microscopic, macroscopic, and network levels.
- Design of traffic control strategies that account for uncertainties in traffic flow.
- Analysis of transportation data using probability and statistics theories.
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "f1c3499440e27b541562733d98d20b94e785079d60d9f6b0ef95d2df6152e784",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}