E 31 St & 3 Ave

99de25d9-5f09-871c-a57e-1856de50e1b3 · temporary removal

Accepted · confidence MediumModel: mr_8a9483

Distance moved

0 m

Capacity change

65 → 65

Pre-window trips

101,301

Match confidence

95%

Pre 2022-05-31 20:00 EDT → 2023-05-30 20:00 EDT

Post 2023-08-31 20:00 EDT → 2024-08-30 20:00 EDT

Before (2022-06-01 – 2023-05-31)

After (2023-09-01 – 2024-08-31)

Neighbor redistribution

Net redistribution ≈ -489 trips

Neighbor	Distance	Observed post	Model pred.	Naive baseline	Residual (model)	Residual (naive)
E 33 St & 1 Ave mzmlvietfr7u	442 m	585.7	757.6	699.1	+171.9	+113.4
2 Ave & E 29 St w7il4y36u3ks	280 m	475.43	552.8	501.4	+77.4	+25.9
E 30 St & Park Ave S 3awxbmf56vl2	290 m	336.07	411.0	380.7	+75.0	+44.7
E 27 St & Park Ave S ixja6iqau5vn	446 m	306.2	370.4	345.3	+64.2	+39.1
E 32 St & Park Ave 4it3ois3el36	275 m	419.03	362.6	321.0	-56.4	-98.0
E 26 St & 3 Ave 2zvw3su3dgx5	397 m	284.02	339.0	307.6	+55.0	+23.6
Lexington Ave & E 29 St 3dhitk2d56tp	229 m	304.84	352.0	691.1	+47.1	+386.3
Lexington Ave & E 36 St bcptasih3c3k	410 m	303.89	345.2	311.5	+41.3	+7.6
Lexington Ave & E 26 St fzf4tkmanut5	412 m	217.21	252.6	229.6	+35.4	+12.4
2 Ave & E 31 St ecq4yxs5pwne	247 m	523.98	501.9	457.7	-22.1	-66.3

Sources

Pre & post trip data: Citi Bike tripdata S3 bucket
Station identity reconciliation: nyc.citibike_simulator_stations_master (canonical & legacy IDs)
Model prediction table: nyc.citibike_simulator_relocation_neighbor_predictions (model_run mr_8a9483)

Detected from ingest run: #94

Download event CSV How the model is evaluated

Methodology and known gaps

This tool is a Concourse research artifact. Not a permit or approval.

What this tool does

Estimates how relocating or resizing a Citi Bike station would affect ridership and rebalancing across a small neighborhood of stations, using public GBFS and S3 trip data.

What this tool does not do

This is not a NYC DOT approval, a Lyft operations tool, or a rebalancing dispatcher. It cannot see rebalancing truck routes, suppressed demand, or pricing history.

Four public-data gaps

Rebalancing truck logs are internal to Lyft.
The product infers rebalancing burden from inventory dynamics, not from observed truck routes. Rebalancing truck movements are not observed and would attenuate these effects in practice.
Suppressed demand (app searches without a trip) is internal to Lyft.
App searches that do not result in a completed trip are not in the public data. Suppressed demand is not observed here.
2019-05 to 2023-06 station-status archive gap.
No public station-status archive covers 2019-05 to 2023-06. Empty/full risk is trained on the OpenBus 2015-2019 window and live GBFS polling from ingest go-live forward. No continuity claim is made across the gap.
Pricing and promotions history is unobserved.
Pricing and promotions history is not observed. It is a confounder that may explain some observed demand shifts.