NL Vehicle Registration Cohorts — by Make/Model, Per Record

Turn the Netherlands' full vehicle-registration register into clean, per-record fleet-size cohorts — one row per (vehicle-type, make, trade-name, first-admission-date) group with a registered-vehicle count, ready to size registered fleets without ever touching a licence plate or owner.

Each source row becomes one clean, flat record with numeric fields coerced to real numbers, a stable source-native record_id, and provenance stamped on every row: source, resource id, the licence notice, the required attribution, a UTC retrieval timestamp and an interpretation caveat.

What one record represents

The source is m9d7-ebf2 — Open Data RDW: Gekentekende_voertuigen on the RDW open-data portal (opendata.rdw.nl). Each record is one registration cohort: a distinct combination of vehicle type, make, trade name and first-admission date, together with registered_count — the number of registered vehicles in that cohort. The count is the only computed value; the licence plate (kenteken) and any owner field are never read, grouped, or delivered.

For each record you get a composite record_id built from the source-native key, the analytic columns listed below (reproduced verbatim, numbers as numbers), and the provenance block. The group columns are voertuigsoort (vehicle type), merk (make), handelsbenaming (trade name) and datum_eerste_toelating (first-admission date, YYYYMMDD), plus registered_count.

Coverage and volume

The register underlying these cohorts holds roughly 16.8 million registered vehicles; grouped to this cohort shape it yields a large, precise set of distinct cohorts. Each record is an anonymous registration fact — a count of vehicles, never a person.

Live count: 5,037,015 grouped cohort records (pinned live via a SODA group-count); the Wave-3 index stated only '>=2,500', so this is the exact figure. The capacity line is the grouped-row count, not the 16.8M source rows.

The Actor pages the source with keyless SODA $query requests ordered by the source-native key for a stable total order, and stops as soon as your Maximum records cap is met. The Actor groups server-side with a SODA $query (SELECT ... , count(*) GROUP BY ...) and never selects the plate or any owner field.

Licence and attribution

RDW open vehicle data is published as public-domain / CC0 open government data. The full notice travels on every record:

RDW (Netherlands Vehicle Authority) open vehicle data - CC0 / public domain. Aggregated registration counts only; the licence plate (kenteken) and any owner/holder field are never read, grouped, or delivered.

The required attribution — RDW - Netherlands Vehicle Authority (Dienst Wegverkeer) — travels on every record.

Interpretation caveat

registered_count is the number of registered vehicles sharing the (vehicle-type, make, trade-name, first-admission-date) cohort. A count of one is an anonymous registration fact, not a person. First-admission date is the date the vehicle was first admitted to the road, not necessarily the Dutch registration date.

Values are reproduced verbatim: the Actor never rescales, re-derives or editorialises a number.

Person-data policy

By construction this Actor reads only aggregate cohort keys and a count. The licence plate (kenteken) and any owner/holder field are never in the query and never in a record — a structural guard rejects any source row that even contains a plate/owner field. No natural-person data is processed.

Data quality and freshness

Numeric fields are coerced from the source's string encoding into real numbers (integers where whole, floats otherwise); genuinely missing cells are delivered as null, never as zero. Text is passed through verbatim. Every run re-reads the live source, so the data is as fresh as the portal itself, and each record's observed_at stamp records exactly when the row was retrieved. Delivery order is fixed by the source-native key, so a capped sample and a later full pull agree on their overlap and a repeated run returns rows in the same order. The RUN_RECEIPT reports source rows scanned and records delivered and charged for a per-run reconciliation.

Provenance, licensing and compliance

Every run begins with a live source-preflight: the Actor reads the exact host's robots.txt at runtime and refuses to proceed if the crawl policy disallows the data path. The gate result — URL, HTTP status, byte length and a SHA-256 of the policy — is written to the run's RUN_RECEIPT, so each run carries its own audit trail. The Actor identifies itself with a transparent, non-impersonating User-Agent and never bypasses a block, solves a challenge, or fetches through a cache or mirror. When the door is genuinely unavailable the run fails loudly and bills nothing.

Inputs

• Maximum records (maxRecords) — hard cap on records delivered and billed. Raise it to pull the full set; lower it to sample cheaply. Records arrive in a stable, source-native order.

Output

Records land in the Actor's default dataset and export as JSON, CSV, Excel or via the Apify API. A tabular overview view surfaces the most useful columns for quick inspection while the full record retains every selected field and provenance stamp.

Fields in detail

The record leads with record_id — a stable composite key drawn from the source's own grain — followed by the analytic columns described above and closed by a provenance block: source, source_dataset (the Socrata resource id), licence, attribution, caveat and observed_at. Every one of those provenance fields is present on every record, so a single row is self-describing: hand it to a colleague or a downstream system and it carries its own origin, licence and retrieval time without reference back to this page. Because delivery is ordered by the source-native key, the same record always carries the same record_id across runs, which makes the dataset safe to diff, deduplicate, or upsert into a warehouse. Nothing in the record is computed or inferred beyond the explicit count where one is stated — every other value is the source's own, reproduced byte-for-byte.

Sibling Actors

This Actor is the registration-cohort view; its sibling nl-vehicle-type-approval-records groups the same register by a finer type-approval / variant key. Two distinct group shapes, two distinct products. Both are distinct from the fleet's eu-vehicle-co2-records cell, which carries CO2 emission values (not registration counts). This Actor also shares its engineering — the runtime robots gate, push-then-charge billing and verbatim-value discipline — with the fleet's other public-data records Actors.

Pricing

This Actor uses Apify's pay-per-event model: a flat $0.05 per record actually delivered to the dataset, and nothing else — no monthly rental, no per-run base fee, no compute charge. Deliver 40 records and you pay $2.00; deliver 10,000 and you pay $500.00. Billing is wired after delivery — each record is pushed first and only then does the per-record event fire — so a mid-run failure can only ever under-charge you, never over-charge. Use Maximum records to cap spend precisely.

Scaling and limits

Set Maximum records low to sample the leading slice cheaply, or high to pull the full set. The Actor paginates server-side and delivers incrementally, so memory stays flat regardless of how many records you request, and you are billed only for what is actually delivered. Because the source is a live public API, extremely deep pagination is ultimately bounded by the source's own paging behaviour; for the vast majority of uses — sampling, a full refresh, or a scheduled top-up — the default paging is more than sufficient. Schedule the Actor on Apify to keep a downstream table current: each run re-reads the live source and re-stamps observed_at, so a nightly or weekly run gives you a dated, reproducible snapshot.

Typical uses

Size registered fleets by make, model and admission cohort; track how a model's registrations build over time; benchmark market share of a make within a vehicle type; or feed an automotive-market, parts-demand or residual-value model with clean cohort counts — all without any owner or plate data.

What this Actor does not do

It does not forecast or model, does not merge multiple source tables into one record, and it never reads or delivers a licence plate or owner field. It gives you faithful, analysis-ready records — with a provenance trail you can audit on every run.