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Transport Ridership Record Quality Audit and Numeric Distribution
x402
Transport Ridership Record Quality Audit and Numeric Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs two analytical reports on up to 200 transport ridership rows: a record quality audit and a numeric distribution analysis for passenger_count or service_count.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution for supplied transport ridership records (passenger_count, service_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two reports for the submitted ridership rows: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed dates/regions; and (2) a numeric distribution analysis of the selected field (passenger_count or service_count), including statistical summaries like min, max, mean, percentiles, or frequency buckets. No external data is fetched — analysis is performed locally on the supplied rows only.
Example use cases
Transit data quality check before reporting
“I have 180 rows of daily ridership records for our regional bus network with passenger_count and service_count values — can you audit them for data quality issues and show me the numeric distribution of passenger_count so I can clean up the dataset before I submit the monthly report?”
Detecting anomalies in service count data
“We just pulled 90 rows of transit service logs across three regions and I'm worried some service_count entries might be null or out of range — run a quality audit and numeric distribution on the service_count field so I can spot any anomalies.”
Validating ridership records from a new data feed
“Our new data provider sent over 200 rows of ridership data and I need to verify quality before we ingest it — please run a record quality audit and numeric distribution on the passenger_count column and tell me what you find.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-004?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of transport ridership data and need both a data quality audit and a numeric distribution summary in a single call at low cost ($0.01 USDC). It is ideal for pre-ingestion validation, data cleaning pipelines, or periodic sanity checks on transit datasets. It does not perform live lookups, predictions, or external data enrichment — use it strictly for offline analysis of supplied records.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, region, date, passenger_count, service_count) cause schema rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric values outside the allowed range (-1e12 to 1e12) are rejected
- Empty rows array returns an error due to minItems:1 constraint
- Specifying an invalid field enum value other than passenger_count or service_count fails validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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