--- license: other license_name: mixed-open-data license_link: https://github.com/MaxHalford/bike-sharing-history pretty_name: DisPatch70 tags: - bike-sharing - urban-computing - spatio-temporal - time-series --- # DisPatch70 Aligned bike-share **station status** and **trip records** for 90+ cities, built for research on urban dispatch / rebalancing simulation. Status snapshots and trips share one directory layout, one file naming scheme, and explicit join keys. ## Layout ``` gbfs/ ///.parquet station status, one row per station x ~15-min snapshot tripdata/ ///.parquet trip records, one row per ride station_dim/crosswalk.parquet station identity table station_dim/snapshots//.parquet sampled raw GBFS station_information over time ``` `gbfs/.../2025/Mar.parquet` and `tripdata/.../2025/Mar.parquet` cover the same city, provider and calendar month (**UTC**; trips are partitioned by `started_at` UTC to match the status feed's `commit_at`). ## Coverage - `gbfs/`: 93 city/provider pairs in 24 countries, 2023-08 (older cities) or 2024-04 (cities added later) through 2026-07, ~15-minute resolution, 349M rows. Source: the public Parquet export of [MaxHalford/bike-sharing-history](https://github.com/MaxHalford/bike-sharing-history) (git-scraped GBFS feeds), re-encoded with alignment keys added. - `tripdata/`: the 12 cities whose operators publish trip records overlapping the status window: New York, Washington DC, Chicago, San Francisco Bay Area, Philadelphia, Montréal, Vancouver, Oslo, Bergen, Mexico City, Guadalajara, Buenos Aires (2023-01 → 2026-07/08 where published). Toronto publishes data but blocks non-North-American downloads; it will be added when fetched. All other systems (JCDecaux, Nextbike, Bird, Beryl, Tembici, Donkey Republic, and most municipal systems outside North America) do not publish trip-level records at all. ## Alignment keys | key | gbfs/ | tripdata/ | note | |---|---|---|---| | station name | `station` | `start_gbfs_name` / `end_gbfs_name` | exact string match | | GBFS station id | `station_id` | `start_station_id` / `end_station_id` | from `station_dim/crosswalk.parquet`; null for JCDecaux feeds (they publish no ids) | | time | `commit_at` (UTC) | `started_at` / `ended_at` (UTC) | local wall time in `started_local`/`ended_local` + `tz` | `start_match`/`end_match` records how each trip endpoint was resolved: `id` (operator id ↔ crosswalk), `name`, `coord` (nearest station ≤ 60 m), or `none`. Trips with `none` and a null `start_station_raw` are dockless e-bike rides that genuinely have no station (~29% of DC and ~22% of Chicago rides); among station-referencing rides, resolution is ≥ 99.7% in every city. ## gbfs/ schema `city, provider, station (name), longitude, latitude, commit_at (UTC), skipped_updates (consecutive unchanged frames before this row), bikes (available bikes), stands (available docks), station_id, capacity_ref (capacity from the crosswalk, last sampled value)` ## tripdata/ schema Unified columns: `city, provider, ride_id, started_at, ended_at (UTC), started_local, ended_local, tz, duration_s, start_station_raw / end_station_raw (operator's id, verbatim), start_station_name / end_station_name (as published), start_lat/lon, end_lat/lon, start_station_id / end_station_id, start_gbfs_name / end_gbfs_name, start_match / end_match, rideable_type, member_type, source_file`, plus every unmapped source column verbatim as `src_` (string). Nothing from the original files is dropped. Per-city id resolution: Lyft cities (NYC/DC/Chicago/SF) match `start_station_raw` to GBFS `short_name`; Oslo/Bergen/Philadelphia/Buenos Aires by digits of `station_id` (`YOS:Station:1009` ↔ `1009`, `bcycle_indego_3213` ↔ `3213`, `277BAEcobici` ↔ `277`); Guadalajara by `station_id`; Mexico City by digits of `short_name`; Montréal by name; Vancouver by the leading `0001`-style code. ## events/ and event_briefings/ ``` events/ //.parquet one row per public event (1,020,642 rows) event_briefings/ //.parquet one LLM-written briefing per event row ``` `events/` unifies public-event records overlapping the status window (2023-01 → 2026-08): city permit/calendar datasets (NYC permitted events incl. street closures, Chicago park permits, SF street closures, Montréal public events, Buenos Aires mass-event permits), ESPN scoreboards for ten leagues matched to venues in the twelve cities, and national public holidays. Columns: `city, source, source_id, category (sports / concert / festival / parade / street_closure / civic / holiday / other), title, description, venue_name, lat, lon, start_utc, end_utc, start_local, end_local, tz, expected_attendance, status` plus every source column verbatim as `src_`. Time precision differs by source (games to the minute, permits as start/end spans, holidays all-day). Coverage is uneven by design: five cities have municipal permit data, all twelve have sports and holidays. `event_briefings/` turns each event row into a 1–3 sentence operational briefing for a dispatcher (where, when, how large, what it means for bike traffic), generated offline with Qwen3-14B from the prompt in the code repository (`prompts/event_briefing.md`). Columns: `city, source, source_id, start_local, description, model, prompt_version`; join back to `events/` on `(city, source, source_id, start_local)`. Descriptions use only the fields of the record; records the model failed to describe are absent rather than filled. ## station_dim/ `crosswalk.parquet`: one row per (city, provider, station): `station_id, first_seen, last_seen, n_snapshots, name_last, names_seen (JSON list of every name observed), short_name, legacy_id, external_id, lat, lon, capacity, region_id, station_type`. Built by sampling the source repo's git history on the 1st and 15th of each month (52 snapshots, 3,003 blobs). ## Caveats - The source git history is nearly empty 2024-02 → 2024-12 and 2025-07 (the archive author squashed it); station identities in those windows come from neighbouring snapshots. - JCDecaux feeds publish no station ids; join those 22 cities by name, or by the numeric prefix many names carry ("1001 - TERREAUX"). - Vancouver trip times are rounded to the hour by the operator (privacy). 2024-02 was published as XLSX and converted to CSV before parsing. - Trips whose local timestamp is ambiguous/nonexistent at DST transitions have `started_at = NaT` and were dropped (≲ 0.1%; counts in the processing logs). - Buenos Aires 2023 has a stray index column preserved as `src_` field; formats drift across years in several cities — `source_file` tells you which raw file each row came from. - `gbfs/` timestamps are commit times of the scraper (~every 15 min), not `last_reported`. ## Provenance & licenses Status: MaxHalford/bike-sharing-history (git scraping of public GBFS/JCDecaux feeds; cite the repo). Trips: the operators' open-data programs (Lyft system-data terms for NYC/DC/Chicago/SF; city open-data licenses for the rest). This dataset redistributes and re-encodes those public files; original values are preserved verbatim in `*_raw`, `*_name` and `src_*` columns.