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Transport Ridership String Normalisation + Two-Measure Correlation
x402
Transport Ridership String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Normalises region/date strings and computes a two-measure Pearson/rank correlation between passenger_count and service_count for up to 200 transport ridership rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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
Two structured analytical reports: (1) a string normalisation report showing standardised region and date values for each input row, and (2) a two-measure correlation report quantifying the statistical relationship between passenger_count and service_count across all supplied rows. No external data is fetched; all analysis is performed on the supplied records only.
Example use cases
Regional bus ridership correlation study
“I've got 80 rows of daily bus data across multiple regions with columns for date, region, passenger_count, and service_count — please normalise the region labels and tell me how strongly passenger volume correlates with the number of services run.”
Transit planning data quality check
“Before presenting this to the city council, can you clean up the region strings in my 120-row transit dataset and produce a two-measure correlation report showing whether more service runs actually bring more passengers?”
Multi-city rail ridership analysis
“Here are 200 rows of rail ridership records across group-a and group-b cities — normalise the date and region fields and run a correlation between passenger_count and service_count so I can see which markets respond most to added service.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-033?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a local batch of up to 200 transport ridership records and need both string normalisation (cleaning region/date labels) and a correlation analysis between two numeric measures (passenger_count and service_count) in a single cheap call. It is ideal when you do not need live external data, predictions, or professional assessments — only local analytical computation on supplied data. Prefer it over generic statistics libraries when you want a bundled, pay-per-use API that handles both data cleaning and correlation in one step at a fixed low cost.
Side effects
- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, region, date, passenger_count, or service_count) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- All passenger_count or service_count values are null — correlation cannot be computed
- Malformed JSON body — request rejected
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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