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Transport Ridership Data Quality Audit, String Normalisation & Trailing Mean Series
x402
Transport Ridership Data Quality Audit, String Normalisation & Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Performs three bundled analytical reports on transport ridership records: record quality audit, string normalisation, and trailing mean time-series computation for passenger_count or service_count.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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
Returns three independent reports: (1) a record quality audit flagging nulls, out-of-range values, and structural issues in passenger_count and service_count; (2) a string normalisation report on region values; and (3) a trailing mean time series for the chosen field (passenger_count or service_count) computed over the specified window size. All reports are generated locally without external lookups.
Example use cases
Pre-load ridership data QA check
“Before I load this month's bus ridership records into our dashboard, can you run a full quality audit on these 120 rows checking passenger_count and service_count for issues, normalise the region names, and compute a trailing mean over a window of 5 for passenger_count?”
Rolling average for transit planning
“I've got 90 rows of daily subway ridership data with region, date, passenger_count, and service_count — can you calculate a 14-period trailing mean on service_count so I can see the smoothed trend over time?”
Cleaning messy region labels in transit exports
“Our transit export has inconsistent capitalisation and spacing in the region field across 60 rows — can you run the string normalisation report on these records along with the quality audit so I can see what needs fixing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations on transport ridership data (audit + normalisation + trailing mean) in a single low-cost call. Ideal for pre-ingestion data pipeline checks on datasets up to 200 rows. Best suited when no live data lookup, prediction, or external enrichment is needed — purely local analysis on supplied records.
Side effects
- Exceeds 200 row limit — request rejected
- Missing required fields (row_id, region, date, passenger_count, or service_count) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Window size outside allowed range (2–200) — parameter error
- All numeric values null — trailing mean series may be empty or undefined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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