Traffic

Telraam

/traffic/telraam

GEOJSON APPLICATION/JSON

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.

Refresh: Every 5 minutes

From

2024-08-21 14:59:39

To

2026-10-11 00:12:21

Records

190,214

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')

Tunnels

/traffic/tunnels

JSON APPLICATION/JSON

The realtime traffic data for the tunnels of Brussels

Refresh: Every minute

From

2024-08-21 14:55:38

To

2026-10-11 00:14:37

Records

1,049,070

import requests

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/tunnels"

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

# Show real-time traffic per tunnel device
for device_id, device_data in data['data'].items():
    results = device_data['results']
    # Get 15-minute aggregate
    r = results['15m']
    for lane, stats in r.items():
        if stats['speed'] is None:          # no measurement for this lane in the period
            continue
        print(f"{device_id} ({lane}): {stats['count']} vehicles, "
              f"speed={stats['speed']:.0f} km/h, occupancy={stats['occupancy']:.1f}%")

Tunnel devices

/traffic/tunnel-devices

GEOJSON APPLICATION/JSON

The position of the traffic devices in the tunnels of Brussels

Refresh: Daily

From

2024-08-22 06:00:01

To

2026-10-10 06:00:00

Records

701

import requests
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/tunnel-devices"

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

gdf = gpd.GeoDataFrame.from_features(data["features"])

# List all tunnel monitoring devices
for _, device in gdf.iterrows():
    print(f"{device['traverse_name']}: {device['descr_fr']}")

# Plot device locations on a map
gdf.plot(figsize=(10, 8), color="orange", markersize=20)

Bike count

/traffic/bike-count

JSON APPLICATION/JSON

The bike count data for the different bike counters in Brussels

Refresh: Every minute

From

2024-08-21 14:55:38

To

2026-10-11 00:14:36

Records

1,049,362

import requests

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/bike-count"

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

# Show bike counts per counter (hourly, daily, yearly)
for counter_id, counter_data in data['data'].items():
    hourly = counter_data.get('hour_cnt', 0)
    daily = counter_data.get('day_cnt', 0)
    yearly = counter_data.get('year_cnt', 0)
    print(f"{counter_id}: {hourly} this hour, {daily} today, {yearly} this year")

Bike counters

/traffic/bike-counters

GEOJSON APPLICATION/JSON

The position of the bike counters in Brussels

Refresh: Daily

From

2024-08-22 06:00:00

To

2026-10-10 06:00:00

Records

701

import requests
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/bike-counters"

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

gdf = gpd.GeoDataFrame.from_features(data["features"])

# List all active bike counters with their location
active = gdf[gdf['active'] == True]
for _, counter in active.iterrows():
    print(f"{counter['device_name']}: {counter.get('road_fr', 'N/A')}")

# Plot counter locations
active.plot(figsize=(10, 8), color="green", markersize=30)

Bus speed

/traffic/bus-speed

JSON APPLICATION/JSON

The speed of the vehicles of STIB/MIVB over their last move, per line, stop and direction: STIB refreshes a vehicle's distance about every 40 seconds, and a move is timed from when the previous distance first appeared to when the new one did.

Refresh: Every 20 seconds (computed from STIB vehicle-distance)

From

2024-08-21 02:00:04

To

2026-10-11 00:14:55

Records

3,145,467

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/traffic/bus-speed"

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

df = pd.DataFrame(data)

# Average bus speed per line
speed_per_line = df.groupby('lineId')['speed'].mean().sort_values()
print("Average bus speed per line (km/h):")
print(speed_per_line)

# Find the slowest segments
slowest = df.nsmallest(5, 'speed')[['lineId', 'pointId', 'speed']]
print("\nSlowest bus segments:")
print(slowest)

Events

/traffic/events

GEOJSON APPLICATION/JSON

Real-time traffic events in the Brussels-Capital Region (roadworks, accidents, congestion, etc.).

Refresh: Every 5 minutes

From

2026-03-30 11:05:24

To

2026-10-11 00:12:19

Records

47,822

import requests
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/events"

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

gdf = gpd.GeoDataFrame.from_features(data["features"])

# Show active traffic events
active = gdf[gdf['is_active'] == True]
for _, event in active.head(10).iterrows():
    print(f"[{event['type_fr']}] {event['location_fr']} — {event['consequences_fr']}")

# Plot active events on a map
active.plot(figsize=(10, 8), color="red", markersize=15)

Tmc event codes

/traffic/tmc-event-codes

GEOJSON APPLICATION/JSON

TMC (Traffic Message Channel) event codes reference data used for classifying traffic events in Brussels.

Refresh: Weekly

From

2026-04-29 11:31:02

To

2026-10-05 18:37:21

Records

4

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/traffic/tmc-event-codes"

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

# Build a lookup table of TMC event codes
codes = pd.DataFrame([f['properties'] for f in data['features']])
print("TMC Event Codes:")
print(codes[['datex_code', 'descr_en', 'datex2_class']].to_string(index=False))

Endpoints

Telraam

Tunnels

Tunnel devices

Bike count

Bike counters

Bus speed

Events

Tmc event codes