Telraam

Traffic: counters, message signs, events and car parks

Format
GeoJSON
Updates
Every 5 minutes
Latest snapshot
Snapshots stored
about 211,023
curl -H "Authorization: Bearer $MOBILITYTWIN_TOKEN" "https://api.mobilitytwin.brussels/traffic/telraam"
Get a token

The Telraam counts of the Brussels segments, one feature per segment: date is the start of the last completed UTC hour, published about 15 minutes after that hour ends. A segment that sent no report for that hour has date "NaT" and empty counts.

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 264 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

NameMeaning
timestampReturn 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.

Response fields

A GeoJSON FeatureCollection. Field names without a prefix are in each feature's properties.

FieldType
bikenumber or string
carnumber or string
datestring
heavynumber or string
last_data_packagestring
pedestriannumber or string
periodstring
segment_idinteger
timezonestring
uptimenumber or string
v85number or string
messagestring
status_codeinteger

Python example

import requests
import pandas as pd
import geopandas as gpd

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/traffic/telraam"

data = requests.get(url, headers={
        'Authorization': 'Bearer [MY_API_KEY]'
}, params=params).json()

gdf = gpd.GeoDataFrame.from_features(data["features"])
# Segments without a report for the hour have empty counts (and date "NaT")
for column in ['car', 'heavy', 'bike', 'pedestrian', 'v85']:
    gdf[column] = pd.to_numeric(gdf[column], errors='coerce')

# Analyze traffic composition per segment
for _, seg in gdf.dropna(subset=['car', 'v85']).head(5).iterrows():
    print(f"Segment {seg['segment_id']}: {seg['car']:.0f} cars, {seg['bike']:.0f} bikes, "
          f"{seg['heavy']:.0f} heavy, v85={seg['v85']:.0f} km/h")

# Plot segments colored by speed
gdf.plot(figsize=(12, 10), column='v85', legend=True, linewidth=2, cmap='RdYlGn')