Data systemsFoundational
Real-Time Transit Data Collection Loops
A polling loop turns a live vehicle feed into an analyzable historical dataset.
Site connection
The Rutgers Bus Analysis project polled PassioGO every 30 seconds and collected hundreds of thousands of data points.
Visual model
Repeated polling becomes a time series
The chart stands in for route observations accumulating across the day.
Interactive
Class schedules create visible transit demand pulses
The Loop
A collector calls the API, timestamps the response, normalizes fields, writes records, waits, and repeats.
The important design detail is consistency: the same polling interval and schema make later analysis much easier.
Operational Concerns
Long-running collectors need retries, logs, disk checks, and a plan for API failures. A week of data is only useful if gaps are visible.
Common Pitfalls
- Ignoring failed polls.
- Changing schemas mid-collection without versioning.
- Assuming every vehicle reports at the same cadence.
Quick check
Quiz
Why timestamp each API response?
- To make file names cute
- To reconstruct route behavior over time
- To avoid storing route IDs
- To remove missing data
Transit analysis depends on ordering observations in time.