Campus Bus Load Curves by Time of Day
Transit demand becomes legible when bus pings and load observations are aggregated into daily route curves.
Site connection
The Rutgers Bus Analysis project collected PassioGO data and analyzed route load, speed, loop time, and capacity patterns.
Visual model
Route demand pulses
Move through the day and watch route loads peak at different times.
Interactive
Class schedules create visible transit demand pulses
From Pings to Curves
A real-time bus feed produces many small observations: bus identity, route, position, timestamp, and sometimes load.
A load curve groups those observations by route and time bucket so patterns become visible.
Why Time of Day Matters
Campus transit is shaped by synchronized class schedules. Ten minutes before and after class changes can look very different from the middle of a lecture block.
A route can be efficient on average while still failing during narrow peaks.
| Metric | Question it answers |
|---|---|
| Average load | How crowded is the route? |
| Loop time | How long does a bus take to return? |
| Speed | Where does the route slow down? |
| Active buses | Is supply matching demand? |
Common Pitfalls
- Averaging across the full day and hiding class-change spikes.
- Treating GPS pings as evenly spaced when polling or connectivity can vary.
- Comparing routes without considering route length.
- Ignoring missing data during outages or low-signal periods.
Quick check
Quiz
Why bucket bus observations by time?
- To remove all uncertainty
- To reveal daily demand patterns
- To hide route differences
- To avoid plotting data
Time buckets turn noisy pings into route-level curves that can be compared.