Case study 02
Business intelligence · Transportation
In development · Blueprint published
Houston METRO Delay Intelligence
A planned operations analytics system for identifying delay patterns, governing reliability KPIs, and turning transit data into decision-support insights.
- Python
- SQL
- GTFS
- Statistics
- Predictive modeling
- QuickSight
- v0.1
- project blueprint
- GTFS
- planned source validation
- BIE
- operations decision support
01 · Business question
What needed to change?
Published schedules describe intended service, but operating conditions may cause buses to deviate from the plan. The project asks how official transit and contextual data can support defensible reliability KPIs, recurring-pattern analysis, and early delay-risk signals without overstating what the available data proves.
02 · Approach
From ambiguity to a system.
- 01
Validate official schedule, near-real-time, alert, weather, and candidate traffic sources before implementation.
- 02
Separate raw snapshot grain from KPI outcome grain and define explicit prediction cutoffs to prevent double-counting and leakage.
- 03
Build conformed operational tables, documented business rules, and reusable SQL metrics with data-quality checks.
- 04
Use statistical analysis to quantify patterns, uncertainty, and observed associations across routes and operating periods.
- 05
Compare predictive candidates with transparent baselines using chronological validation and segment-level error reporting.
03 · Analytical flow
A repeatable path from question to action.
- 01Official sources
- 02Historical capture
- 03Operational model
- 04Governed KPI layer
- 05Decision support
04 · Outcome
A transparent roadmap from raw transit data to governed reliability and predictive insights.
- Documents the source-validation, architecture, KPI, statistical, and modeling decisions before implementation begins.
- Makes the distinction between arrival predictions, estimated schedule deviation, and validated actual delay explicit.
- Frames predictive modeling as one component of an operations decision-support system rather than the entire project.
Reflection
“The blueprint starts with trustworthy definitions, source limitations, and decision context because a useful model depends on all three.”