Datasets:
station_id int64 1k 34k | status stringclasses 2 values | capacity int16 3 155 ⌀ | bikes_total int8 0 107 ⌀ | bikes_mechanical int8 0 76 ⌀ | bikes_electric int8 0 51 ⌀ | stands_free int16 0 152 ⌀ | departures int8 0 80 | arrivals int8 0 69 | horodate timestamp[ns, tz=UTC]date 2023-01-01 00:00:00 2025-12-31 23:00:00 |
|---|---|---|---|---|---|---|---|---|---|
1,001 | OPEN | 16 | 16 | 12 | 4 | 0 | 0 | 1 | 2023-01-01T00:00:00 |
1,001 | OPEN | 16 | 15 | 11 | 4 | 1 | 2 | 1 | 2023-01-01T00:30:00 |
1,001 | OPEN | 16 | 16 | 11 | 5 | 0 | 0 | 1 | 2023-01-01T01:00:00 |
1,001 | OPEN | 16 | 16 | 11 | 5 | 0 | 0 | 0 | 2023-01-01T01:30:00 |
1,001 | OPEN | 16 | 15 | 10 | 5 | 1 | 1 | 0 | 2023-01-01T02:00:00 |
1,001 | OPEN | 16 | 15 | 10 | 5 | 1 | 1 | 1 | 2023-01-01T02:30:00 |
1,001 | OPEN | 16 | 14 | 9 | 5 | 2 | 2 | 1 | 2023-01-01T03:00:00 |
1,001 | OPEN | 16 | 15 | 10 | 5 | 1 | 0 | 1 | 2023-01-01T03:30:00 |
1,001 | OPEN | 16 | 16 | 10 | 6 | 0 | 1 | 2 | 2023-01-01T04:00:00 |
1,001 | OPEN | 16 | 15 | 9 | 6 | 1 | 1 | 0 | 2023-01-01T04:30:00 |
1,001 | OPEN | 16 | 16 | 10 | 6 | 0 | 1 | 2 | 2023-01-01T05:00:00 |
1,001 | OPEN | 16 | 12 | 7 | 5 | 4 | 4 | 0 | 2023-01-01T05:30:00 |
1,001 | OPEN | 16 | 13 | 8 | 5 | 3 | 1 | 2 | 2023-01-01T06:00:00 |
1,001 | OPEN | 16 | 13 | 8 | 5 | 3 | 0 | 0 | 2023-01-01T06:30:00 |
1,001 | OPEN | 16 | 13 | 8 | 5 | 3 | 0 | 0 | 2023-01-01T07:00:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 3 | 0 | 2023-01-01T07:30:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T08:00:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T08:30:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T09:00:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T09:30:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T10:00:00 |
1,001 | OPEN | 16 | 10 | 5 | 5 | 6 | 0 | 0 | 2023-01-01T10:30:00 |
1,001 | OPEN | 16 | 9 | 5 | 4 | 7 | 1 | 0 | 2023-01-01T11:00:00 |
1,001 | OPEN | 16 | 11 | 6 | 5 | 5 | 0 | 2 | 2023-01-01T11:30:00 |
1,001 | OPEN | 16 | 13 | 8 | 5 | 3 | 1 | 3 | 2023-01-01T12:00:00 |
1,001 | OPEN | 16 | 14 | 9 | 5 | 2 | 0 | 1 | 2023-01-01T12:30:00 |
1,001 | OPEN | 16 | 10 | 7 | 3 | 6 | 4 | 0 | 2023-01-01T13:00:00 |
1,001 | OPEN | 16 | 9 | 6 | 3 | 7 | 2 | 1 | 2023-01-01T13:30:00 |
1,001 | OPEN | 16 | 8 | 4 | 4 | 8 | 2 | 1 | 2023-01-01T14:00:00 |
1,001 | OPEN | 16 | 9 | 4 | 5 | 7 | 1 | 2 | 2023-01-01T14:30:00 |
1,001 | OPEN | 16 | 11 | 6 | 5 | 5 | 1 | 3 | 2023-01-01T15:00:00 |
1,001 | OPEN | 16 | 7 | 2 | 5 | 9 | 4 | 0 | 2023-01-01T15:30:00 |
1,001 | OPEN | 16 | 10 | 4 | 6 | 6 | 2 | 5 | 2023-01-01T16:00:00 |
1,001 | OPEN | 16 | 11 | 4 | 7 | 5 | 0 | 1 | 2023-01-01T16:30:00 |
1,001 | OPEN | 16 | 12 | 4 | 8 | 4 | 1 | 2 | 2023-01-01T17:00:00 |
1,001 | OPEN | 16 | 13 | 5 | 8 | 3 | 1 | 2 | 2023-01-01T17:30:00 |
1,001 | OPEN | 16 | 14 | 5 | 9 | 2 | 0 | 1 | 2023-01-01T18:00:00 |
1,001 | OPEN | 16 | 15 | 7 | 8 | 1 | 1 | 2 | 2023-01-01T18:30:00 |
1,001 | OPEN | 16 | 15 | 7 | 8 | 1 | 1 | 1 | 2023-01-01T19:00:00 |
1,001 | OPEN | 16 | 13 | 5 | 8 | 3 | 3 | 1 | 2023-01-01T19:30:00 |
1,001 | OPEN | 16 | 14 | 6 | 8 | 2 | 0 | 1 | 2023-01-01T20:00:00 |
1,001 | OPEN | 16 | 16 | 8 | 8 | 0 | 0 | 2 | 2023-01-01T20:30:00 |
1,001 | OPEN | 16 | 16 | 8 | 8 | 0 | 0 | 0 | 2023-01-01T21:00:00 |
1,001 | OPEN | 16 | 16 | 8 | 8 | 0 | 0 | 0 | 2023-01-01T21:30:00 |
1,001 | OPEN | 16 | 14 | 6 | 8 | 2 | 2 | 0 | 2023-01-01T22:00:00 |
1,001 | OPEN | 16 | 14 | 6 | 8 | 2 | 0 | 0 | 2023-01-01T22:30:00 |
1,001 | OPEN | 16 | 13 | 5 | 8 | 3 | 1 | 0 | 2023-01-01T23:00:00 |
1,001 | OPEN | 16 | 14 | 6 | 8 | 2 | 0 | 1 | 2023-01-01T23:30:00 |
1,001 | OPEN | 16 | 16 | 6 | 10 | 0 | 0 | 2 | 2023-01-02T00:00:00 |
1,001 | OPEN | 16 | 16 | 6 | 10 | 0 | 0 | 0 | 2023-01-02T00:30:00 |
1,001 | OPEN | 16 | 16 | 6 | 10 | 0 | 0 | 0 | 2023-01-02T01:00:00 |
1,001 | OPEN | 16 | 16 | 6 | 10 | 0 | 0 | 0 | 2023-01-02T01:30:00 |
1,001 | OPEN | 16 | 15 | 5 | 10 | 1 | 1 | 0 | 2023-01-02T02:00:00 |
1,001 | OPEN | 16 | 15 | 5 | 10 | 1 | 0 | 0 | 2023-01-02T02:30:00 |
1,001 | OPEN | 16 | 14 | 4 | 10 | 2 | 1 | 0 | 2023-01-02T03:00:00 |
1,001 | OPEN | 16 | 14 | 4 | 10 | 2 | 0 | 0 | 2023-01-02T03:30:00 |
1,001 | OPEN | 16 | 14 | 4 | 10 | 2 | 0 | 0 | 2023-01-02T04:00:00 |
1,001 | OPEN | 16 | 14 | 4 | 10 | 2 | 0 | 0 | 2023-01-02T04:30:00 |
1,001 | OPEN | 16 | 13 | 4 | 9 | 3 | 1 | 0 | 2023-01-02T05:00:00 |
1,001 | OPEN | 16 | 13 | 4 | 9 | 3 | 0 | 0 | 2023-01-02T05:30:00 |
1,001 | OPEN | 16 | 13 | 3 | 10 | 3 | 1 | 1 | 2023-01-02T06:00:00 |
1,001 | OPEN | 16 | 11 | 2 | 9 | 5 | 2 | 0 | 2023-01-02T06:30:00 |
1,001 | OPEN | 16 | 11 | 2 | 9 | 5 | 0 | 0 | 2023-01-02T07:00:00 |
1,001 | OPEN | 16 | 7 | 2 | 5 | 9 | 5 | 1 | 2023-01-02T07:30:00 |
1,001 | OPEN | 16 | 5 | 1 | 4 | 11 | 4 | 2 | 2023-01-02T08:00:00 |
1,001 | OPEN | 16 | 3 | 1 | 2 | 13 | 2 | 0 | 2023-01-02T08:30:00 |
1,001 | OPEN | 16 | 3 | 1 | 2 | 13 | 1 | 1 | 2023-01-02T09:00:00 |
1,001 | OPEN | 16 | 1 | 1 | 0 | 15 | 2 | 0 | 2023-01-02T09:30:00 |
1,001 | OPEN | 16 | 4 | 4 | 0 | 12 | 0 | 3 | 2023-01-02T10:00:00 |
1,001 | OPEN | 16 | 3 | 2 | 1 | 13 | 2 | 1 | 2023-01-02T10:30:00 |
1,001 | OPEN | 16 | 5 | 3 | 2 | 11 | 0 | 2 | 2023-01-02T11:00:00 |
1,001 | OPEN | 16 | 7 | 5 | 2 | 9 | 1 | 3 | 2023-01-02T11:30:00 |
1,001 | OPEN | 16 | 8 | 6 | 2 | 8 | 2 | 3 | 2023-01-02T12:00:00 |
1,001 | OPEN | 16 | 9 | 6 | 3 | 7 | 0 | 1 | 2023-01-02T12:30:00 |
1,001 | OPEN | 16 | 9 | 6 | 3 | 7 | 3 | 3 | 2023-01-02T13:00:00 |
1,001 | OPEN | 16 | 8 | 5 | 3 | 8 | 1 | 0 | 2023-01-02T13:30:00 |
1,001 | OPEN | 16 | 9 | 7 | 2 | 7 | 1 | 2 | 2023-01-02T14:00:00 |
1,001 | OPEN | 16 | 10 | 7 | 3 | 6 | 3 | 4 | 2023-01-02T14:30:00 |
1,001 | OPEN | 16 | 15 | 12 | 3 | 1 | 0 | 5 | 2023-01-02T15:00:00 |
1,001 | OPEN | 16 | 15 | 12 | 3 | 1 | 2 | 2 | 2023-01-02T15:30:00 |
1,001 | OPEN | 16 | 16 | 14 | 2 | 0 | 2 | 3 | 2023-01-02T16:00:00 |
1,001 | OPEN | 16 | 15 | 13 | 2 | 1 | 1 | 0 | 2023-01-02T16:30:00 |
1,001 | OPEN | 16 | 16 | 12 | 4 | 0 | 2 | 3 | 2023-01-02T17:00:00 |
1,001 | OPEN | 16 | 14 | 10 | 4 | 2 | 2 | 0 | 2023-01-02T17:30:00 |
1,001 | OPEN | 16 | 12 | 8 | 4 | 4 | 2 | 0 | 2023-01-02T18:00:00 |
1,001 | OPEN | 16 | 12 | 8 | 4 | 4 | 1 | 1 | 2023-01-02T18:30:00 |
1,001 | OPEN | 16 | 11 | 7 | 4 | 5 | 1 | 0 | 2023-01-02T19:00:00 |
1,001 | OPEN | 16 | 6 | 2 | 4 | 10 | 6 | 1 | 2023-01-02T19:30:00 |
1,001 | OPEN | 16 | 8 | 4 | 4 | 8 | 0 | 2 | 2023-01-02T20:00:00 |
1,001 | OPEN | 16 | 10 | 4 | 6 | 6 | 0 | 2 | 2023-01-02T20:30:00 |
1,001 | OPEN | 16 | 12 | 6 | 6 | 4 | 0 | 2 | 2023-01-02T21:00:00 |
1,001 | OPEN | 16 | 12 | 6 | 6 | 4 | 0 | 0 | 2023-01-02T21:30:00 |
1,001 | OPEN | 16 | 12 | 6 | 6 | 4 | 0 | 0 | 2023-01-02T22:00:00 |
1,001 | OPEN | 16 | 13 | 6 | 7 | 3 | 0 | 1 | 2023-01-02T22:30:00 |
1,001 | OPEN | 16 | 14 | 7 | 7 | 2 | 0 | 1 | 2023-01-02T23:00:00 |
1,001 | OPEN | 16 | 14 | 7 | 7 | 2 | 0 | 0 | 2023-01-02T23:30:00 |
1,001 | OPEN | 16 | 14 | 7 | 7 | 2 | 0 | 0 | 2023-01-03T00:00:00 |
1,001 | OPEN | 16 | 16 | 9 | 7 | 0 | 0 | 2 | 2023-01-03T00:30:00 |
1,001 | OPEN | 16 | 16 | 9 | 7 | 0 | 0 | 0 | 2023-01-03T01:00:00 |
1,001 | OPEN | 16 | 16 | 9 | 7 | 0 | 0 | 0 | 2023-01-03T01:30:00 |
Lyon velo'v Bike Sharing Dataset
Dataset Description
This dataset provides an aggregated view of the Vélo'v bike-sharing station activities in the Lyon Metropolitan area (France) for 2023, 2024, and 2025. Unlike the raw real-time data, this dataset has been preprocessed to offer a 30-minute temporal granularity, including calculated flows (bike arrivals and departures).
Note on Temporal Coverage: The current release ensures a continuous and comprehensive record for all of 2023, 2024, and 2025.
- Original Source: Métropole de Lyon - Open Data Portal.
- License: Etalab Open License.
Usage
You can easily load this dataset using the Hugging Face datasets library:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("lmoncla/lyon-velov-bike-sharing-dataset", split="train")
# Convert to a Pandas DataFrame for analysis
df = dataset.to_pandas()
# Display the first few rows
print(df.head())
Preprocessing Details
The data cleaning and transformation were performed using Python scripts. Key steps include:
- Normalization: Conversion of raw JSON snapshots into a structured tabular format.
- Flow Calculation: Using the difference in total bike counts to identify departures and arrivals for each station.
- Resampling: Time-series aggregation into 30-minute buckets, preserving the last known capacity and summing up movements.
Dataset Schema
station_id: Unique identifier for the bike station.horodate: Timestamp marking the end of the 30-minute interval.capacity: Total number of docks available at the station.bikes_total: Total number of bikes available at the end of the interval.bikes_mechanical/bikes_electric: Breakdown by bike type.departures: Cumulative sum of bike departures within the 30-minute window.arrivals: Cumulative sum of bike arrivals within the 30-minute window.
Citation and Attribution
Original data produced by Métropole de Lyon.
Note: This dataset is a derivative work created for analysis and academic purposes.
If you use this dataset in your research or project, please cite it as follows:
@misc{lyon-velov-bike-sharing-dataset-2026,
author = {Moncla, Ludovic},
title = {Lyon velo'v Bike Sharing Dataset},
year = {2026},
publisher = {Hugging Face},
journal = {Hugging Face Datasets},
howpublished = {\url{[https://huggingface.co/datasets/lmoncla/lyon-velov-bike-sharing-dataset](https://huggingface.co/datasets/lmoncla/lyon-velov-bike-sharing-dataset)}}
}
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