Aggregated speed
STIB: brussels metro, tram and bus operator
- Format
- JSON
- Updates
- Every 20 seconds (10-minute rolling average)
- Latest snapshot
- Snapshots stored
- about 2,727,674
curl -H "Authorization: Bearer $MOBILITYTWIN_TOKEN" "https://api.mobilitytwin.brussels/stib/aggregated-speed"The average speed of STIB/MIVB vehicles over the last 10 minutes, per line, stop and direction.
Which days have data
Data from 21 Aug 2024 to 2 Nov 2026, on 799 days. 30 of the last 30 days have data. A typical day holds 2079 snapshots. Days are in UTC.
2026
JanFebMarAprMayJunJulAugSepOctNovDec
2025
JanFebMarAprMayJunJulAugSepOctNovDec
2024
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.
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. |
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/aggregated-speed"
data = requests.get(url, headers={
'Authorization': 'Bearer [MY_API_KEY]'
}, params=params).json()
df = pd.DataFrame(data)
# Compare average speed across lines over 10-minute intervals
speed_by_line = df.groupby('lineId')['speed'].agg(['mean', 'min', 'max'])
print("Speed statistics per line (km/h):")
print(speed_by_line.sort_values('mean'))