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Bluebikes broken-station detector

Load a month of Bluebikes (the Boston metro bikeshare) trip data and flag stations that probably had a broken dock. A station looks broken when no bikes were returned during an hour where the same station typically receives several. The flag is a Bayesian probability combining a prior on dock failures with a Poisson model of expected returns.

1. Load trip data

Everything stays in your browser. No upload.

Drop one or more YYYYMM-bluebikes-tripdata.zip files here, or choose files

Trip ZIPs are published at bluebikes.com/system-data (hosted on the hubway-data S3 bucket). The S3 bucket blocks browser fetches, so files have to be downloaded manually and then dropped back in here.

2. Inspect

Date

Hour of day (24h)

17:00

Smoothing window

Pools each cell's counts across neighboring hours. Grid shape stays 24 columns.

Prior P(broken) for any one hour

1.0%

Min expected returns (λ) to flag

0.5

Show the math

Each (station, day-of-week, hour-of-day) cell gets its own baseline rate λ. To compute it for a query date, take returns at the same station and same hour-of-day on every other loaded date that fell on the same day-of-week, then average. A month of trip data typically contributes 4 or 5 dates per day-of-week; the queried date is excluded (leave-one-out) so a long outage can't drag its own baseline toward zero. If only one date of that day-of-week is loaded there's no out-of-sample evidence and the cell is ineligible. Returns within a single hour are modeled as a Poisson process, so the probability of zero returns when the dock is working is e^−λ.

Let π be the prior probability that any one hour at any one station is "broken" (default 1%, slide to taste). When the dock is broken, returns are forced to zero. Bayes then gives:

P(broken | 0 returns) = π / (π + (1 − π) · e^−λ)

Increasing the time window pools observed counts and expected counts across ±N hours; the same formula applies to the summed λ. Near hour 0 or 23 the window is clipped (no wrap), so edge hours pool fewer hours and need a proportionally higher baseline to clear the cutoff. Cells where the summed λ is below the cutoff are marked ineligible because you can't distinguish a broken dock from a routinely-quiet hour.

Known gaps. A station broken for the entire loaded window won't appear at all (no trip records means no coordinate and no baseline to compare against). Trip timestamps are local time and daylight-saving isn't accounted for: spring-forward leaves one DOW-hour cell with no observations and fall-back double-counts another, each by one date per year.