Car parks

Traffic: counters, message signs, events and car parks

Format: GeoJSON
Updates: Daily
Latest snapshot: 2 Nov 2026, 04:10 UTC
Snapshots stored: about 11

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

Get a token

The public car parks of Brussels, as published by Brussels Mobility: name, address, operator, contact, capacity and disabledcapacity, number of floors and maximum height (-999 when unknown). There is no occupancy.

Which days have data

Data from 22 Oct 2026 to 2 Nov 2026, on 11 days. 10 of the last 30 days have data. A typical day holds 1 snapshot. 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
`bbox`	array
`crs.properties.name`	string
`crs.type`	string
`features[].bbox`	array
`features[].geometry_name`	string
`active`	boolean
`capacity`	integer
`contact_mail`	string
`contact_phone`	null or string
`datexid`	string
`disabledcapacity`	integer
`extref`	string
`floors`	integer
`gid`	integer
`housenr`	null or string
`lastmodified`	string
`maxheight`	number
`maxwidth`	number
`mu_fr`	string
`mu_nl`	string
`name_fr`	string
`name_nl`	string
`operator_fr`	string
`operator_id`	integer
`operator_nl`	string
`parkingid`	integer
`pccp`	string
`road_fr`	string
`road_nl`	string
`numberReturned`	integer
`timeStamp`	string
`totalFeatures`	string

Python example

import requests
import geopandas as gpd

url = "https://api.mobilitytwin.brussels/traffic/car-parks"

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

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

# The ten largest public car parks
for _, park in gdf.nlargest(10, "capacity").iterrows():
    print(f"{park['name_fr']} ({park['operator_fr']}): {park['capacity']} places")

gdf.plot(figsize=(10, 8), markersize=gdf["capacity"] / 20, color="#1B2A4A")