Analysing Bus Arrival Times — Findings

  • I rush to the bus stop and see the bus leaving — the ‘just missed the bus’ phenomenon
  • I wait at the bus stop for the next couple of minutes seeing the ‘bus arriving’ message — the ‘bus arriving’ phenomenon

Setup

Let’s start with an illustration using 2 bus stops and a simple formula.

[1] Journey time = Arrival at #2 — Arrival at #1

Smooth Traffic

Data captured from Bus Arrival API during smooth traffic
  • At 8:48:04, the bus is predicted to reach at 8:48:37.
  • At 8:49:03, the bus is predicted to reach at 8:48:14. Technically this timing is backdated and should be classified as reporting rather than predicting.
  • At 8:50:03, the next bus is predicted to reach at 9:00:54.
[1a] Predicted journey time 
= predicted arrival at #2 — predicted arrival at #1
[1b] Estimated journey time
= backdated arrival at #2 — backdated arrival at #1

Heavy Traffic

Data captured from Bus Arrival API during heavy traffic
  • At 15:01:05, bus is reported to have arrived at 15:00:16.
  • At 15:02:04, bus is reported to have arrived at 15:00:41 (i.e. best backdated arrival time).
  • At 15:03:04, the bus is reported to have arrived at 15:02:53.
  • At 15:04:04, the bus is reported to have arrived at 15:03:40.
  • At 15:05:05, the bus is reported to have arrived at 15:04:18.
  • At 15:06:05, the bus is reported to have arrived at 15:05:19 (i.e. best backdated arrival time)

Constraint

When buses of the same service arrive in close proximity (i.e. bus bunching), arrival times are affected and interpreting [1b] becomes a challenge.

Conclusion

I have discovered that while backdated arrival times might not be suitable for bus apps, I found them good estimates for recording the arrival times of buses at stops and in turn, preferred parameters for calculating estimated journey times.

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SingTat

SingTat

IT geek who gets inspirations from everyday life and surroundings