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Transport Ridership Data Quality Audit, String Normalisation & Numeric Interval Filter
x402
Transport Ridership Data Quality Audit, String Normalisation & Numeric Interval Filter 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).
Audits record quality, normalises region strings, and filters passenger_count or service_count by numeric interval for up to 200 transport ridership rows, returning three independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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 analytical reports: (1) a record quality audit flagging missing values, malformed dates, or schema violations across the submitted rows; (2) a string normalisation report showing standardised region name variants; (3) a numeric interval filter report listing which rows fall within the specified minimum–maximum range for the chosen field (passenger_count or service_count). All results are derived locally from the submitted data with no external lookups.
Example use cases
Transit agency data quality check
“I have 120 rows of daily ridership data from our regional bus network with passenger_count and service_count fields — can you audit the record quality, normalise the region names, and filter passenger_count values between 10 and 5000 so I can spot bad records before importing them into our dashboard?”
Rail ridership anomaly pre-screening
“Before I load this week's train ridership CSV into our pipeline, please run a quality audit and filter service_count to only rows between 1 and 200, and normalise the region strings — I'm sending 80 rows with row_id, region, date, passenger_count, and service_count.”
Multi-region transit data cleaning
“I've got ridership records from five regions mixed together — about 150 rows — and I need the region strings normalised, a quality audit to catch nulls or formatting issues, and a numeric filter on passenger_count from 0 to 10000. Can you generate all three reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous local analytical operations — quality audit, string normalisation, and numeric interval filtering — on transport ridership data in a single $0.01 call. It is ideal for pre-processing transit datasets before ingestion into dashboards or pipelines, particularly when you want to catch bad records, standardise region labels, and range-filter numeric fields in one pass without making external API calls or incurring per-operation costs.
Side effects
- Submitting more than 200 rows returns an error — batch must be split
- Missing required fields (row_id, region, date, passenger_count, service_count) causes row-level validation failure
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- minimum greater than maximum causes the interval filter to return no matching rows
- Null numeric values for both passenger_count and service_count may degrade numeric filter results
- Payment failure or insufficient USDC balance prevents processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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