Vehicle distance
STIB: brussels metro, tram and bus operator
- Format
- JSON
- Updates
- Every 20 seconds
- Latest snapshot
- Snapshots stored
- about 5,093,864
curl -H "Authorization: Bearer $MOBILITYTWIN_TOKEN" "https://api.mobilitytwin.brussels/stib/vehicle-distance"Raw STIB/MIVB positions as published: each vehicle's distance since its last stop, with no vehicle or trip id. For long time ranges, download Parquet files from /stib/vehicle-distance/parquet.
Which days have data
Data from 24 Feb 2023 to 2 Nov 2026, on 1289 days. 30 of the last 30 days have data. A typical day holds 4110 snapshots. Days are in UTC.
2026
JanFebMarAprMayJunJulAugSepOctNovDec
2025
JanFebMarAprMayJunJulAugSepOctNovDec
2024
JanFebMarAprMayJunJulAugSepOctNovDec
2023
JanFebMarAprMayJunJulAugSepOctNovDec
- Full day
- Partial, 50–90% of a typical day
- Low, under 50%
- No snapshot
- Not expected
Click a day above, or pick a date, to get its requests.
Downloading weeks or months
Do not fetch snapshots one by one for long ranges. /stib/vehicle-distance/parquet gives you ready-made Parquet files, one per day.
Parameters
| Name | Meaning |
|---|---|
timestamp | Return the last snapshot at or before this moment, in Unix seconds or ISO 8601 (e.g. 2026-05-06T12:00:00Z). Without it, the latest snapshot is returned. |
start_timestamp | Start of the range, in Unix seconds or ISO 8601. |
end_timestamp | End of the range, in Unix seconds or ISO 8601. |
limit | Maximum number of snapshots, at most 100. |
offset | Number of snapshots to skip, to page through a range. |
Python example
import requests
import pandas as pd
from datetime import datetime, timedelta, timezone
yesterday_noon = (datetime.now(timezone.utc) - timedelta(days=1)).replace(
hour=12, minute=0, second=0, microsecond=0
)
params = {
"timestamp": yesterday_noon.isoformat(), # ISO accepted; epoch also works
}
url = "https://api.mobilitytwin.brussels/stib/vehicle-distance"
data = requests.get(url, headers={
'Authorization': 'Bearer [MY_API_KEY]'
}, params=params).json()
# Convert to DataFrame for analysis
df = pd.DataFrame(data)
# Group by line and compute average distance from stop
avg_distance = df.groupby('lineId')['distanceFromPoint'].mean()
print("Average distance from stop per line:")
print(avg_distance.sort_values(ascending=False))