Events

Traffic: counters, message signs, events and car parks

Format: GeoJSON
Updates: Every 5 minutes
Latest snapshot: 2 Nov 2026, 22:38 UTC
Snapshots stored: about 56,364

curl -H "Authorization: Bearer $MOBILITYTWIN_TOKEN" "https://api.mobilitytwin.brussels/traffic/events"

Get a token

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

Which days have data

Data from 30 Mar 2026 to 2 Nov 2026, on 218 days. 30 of the last 30 days have data. A typical day holds 262 snapshots. Days are in UTC.

2026

JanFebMarAprMayJunJulAugSepOctNovDec

Full day
Partial, 50–90% of a typical day
Low, under 50%
No snapshot
Not expected

Get a day

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.

Response fields

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

Field	Type
`fid`	integer
`datex_codes`	string
`type_fr`	string
`type_nl`	string
`location_fr`	string
`location_nl`	string
`direction_fr`	string
`direction_nl`	string
`road_type_fr`	string
`road_type_nl`	string
`importance`	string
`consequences_fr`	string
`consequences_nl`	string
`impacted_lanes`	string or null
`start_time`	string
`end_time`	string
`last_update`	string
`is_active`	boolean
`zone_fr`	string or null
`zone_nl`	string or null
`orientation`	string

Python example

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)