Day 1 — Data & Analytics Foundations
9:00 – 9:15
- Introductions & Course Overview
9:15 – 10:30
- The Congestion Problem — Why Data Science for Transportation
- The Data Science Pyramid
10:30 – 10:45
- Break
10:45 – 12:00
- Geospatial Data Overview, KNIME introduction - Why Low-Code?
12:00 – 1:00
- Lunch
1:00 – 2:45
- KNIME Exercise 1
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- Rockridge Window Sensors — Pulling Apart the Data to Evaluate the Problem – Edward Hoffman
2:45 – 3:00
- Break
3:00 – 4:30
- Project Discussion: Select Group Projects, Assign group projects
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- Use Case: Estimating Bike Lane Usage – Anthony Patire, Research Scientist, PATH UC Berkeley
4:30 – 5:00
- Day 1 Summary
DAY 2 — Geospatial, Temporal Big Data
9:00 – 10:00
- Data Preprocessing & Exploratory Data Analysis (EDA) Maps as a Framework
10:00 – 10:15
- Break
10:15 – 11:15
- Data Driven Decision Making – Ramses Madou, Division Manager of Planning, Policy, and Sustainability for the Department of Transportation in the City of San Jose
- Trajectory Analytics
12:00 – 1:00|
- Lunch
1:00 – 2:45
- KNIME Exercise 2
- GPS Probe Data
2:45 – 3:00
- Break
3:00 – 4:30
- Spatial Databases & Map Matching
- Knime Example 2:
- Simulated link volumes + PeMS sensor counts; Compute error metrics; Map where the model diverges from reality
4:30 – 5:00Day 2
- Summary & Project Work Time
DAY 3 — Simulation & Modeling
9:00 – 10:00
- Transportation Network Simulation
- Mobiliti – High Performance Computing and Parallel Discrete Event Simulation
10:00 – 10:15
- Break
10:15 – 12:00
- Use Case: Camp Fire Evacuation Planning
- Leg event analytics
- Use Case: Equitable EV Charging — Babur & Macfarlane (2026)
- Route analytics
- Regional modeling, equity priority communities, emission reductions
12:00 – 1:00
- Lunch
1:00 – 2:30
- Use Case: Knime Example for New Jersey Probe Data Example
2:30 – 2:45
- Break
2:45 – 4:30
- KNIME Exercise 3: Selected Group Project
4:30 – 5:00
- Day 3 Summary & Project Work Time
DAY 4 August 6 — AI, ML & Project Readouts
9:00 – 9:15
- Data Science Pyramid — Review
9:15 – 10:00
- Synthetic Populations in Motion – ActivitySim + Mobiliti
10:00 – 10:15
- Break
10:15 – 11:00
- Knime Examples of AI in Transportation Ontologies, Knowledge Bases & Generative AI
- Green Field Cities
11:00 – 12:00
- Project Readouts
- Each group: 5–7 min presentation + Q&A
12:00 – 1:00
- Lunch
1:00 – 3:00+
- Open Lab (optional)
- Continue Your Project · Dig Deeper into Your Data · Peer Collaboration