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Transport Ridership Data Quality Suite — Audit, Normalisation & Deduplication
x402
Transport Ridership Data Quality Suite — Audit, Normalisation & Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three data-quality reports (record quality audit, string normalisation, and exact row deduplication) on up to 200 transport ridership rows containing passenger and service counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied transport ridership records (passenger_count, service_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent reports delivered together: (1) a record quality audit flagging malformed, null, or out-of-range values in passenger_count and service_count; (2) a string normalisation report showing standardised versions of region strings; (3) an exact row deduplication report identifying and listing duplicate rows. All reports are derived purely from the supplied data with no external lookups.
Example use cases
Pre-analysis ridership data cleanup
“I've got a batch of daily transit ridership records from multiple regions and I need to clean them up before importing into our dashboard — can you audit the record quality, normalise the region name strings, and remove any exact duplicate rows? The data has row_id, region, date, passenger_count, and service_count columns.”
Transit agency data validation pipeline
“We receive weekly ridership feeds from our regional operators and need a quality gate before they hit our database — please run a record quality audit, string normalisation, and exact deduplication check on this batch of up to 200 rows with passenger_count and service_count fields.”
Detecting dirty data in merged ridership exports
“I merged ridership exports from three different bus operators and I'm worried about inconsistent region labels and duplicate entries — can you normalise the strings, flag duplicate rows, and give me a record quality audit for the combined dataset?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three data-quality operations — record audit, string normalisation, and exact deduplication — applied to transport ridership data in a single cost-efficient call ($0.01 USDC for all three reports). It is ideal for batch pre-processing pipelines of up to 200 rows before loading into analytics systems, and when you need offline, deterministic quality checks with no external data dependencies or side effects.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, region, date, passenger_count, service_count) causes rejection
- Dates not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Empty rows array (minItems: 1 not met) causes a schema error
- Payment of 0.01 USDC not provided results in 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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