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Transport Ridership Analytics: Adjacent Row Changes + Weighted Mean
x402
Transport Ridership Analytics: Adjacent Row Changes + Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Computes adjacent row-over-row changes and a secondary-measure weighted mean across up to 200 transport ridership records (passenger_count, service_count), returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean 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 analytical reports: (1) adjacent row-over-row changes for each consecutive pair of supplied ridership records on the chosen field, and (2) a secondary-measure weighted mean of the chosen field (passenger_count or service_count) weighted by the other measure — all computed locally on the supplied data with no external lookups.
Example use cases
Daily bus ridership trend analysis
“I have daily passenger and service counts for two bus regions over the past week — can you compute the day-over-day ridership changes and the service-weighted average passenger count across all rows? I'll send the data as rows with row_id, date, region, passenger_count, and service_count.”
Rail network capacity monitoring
“Here are 50 rows of rail ridership records by region and date with passenger_count and service_count — run the adjacent row changes and secondary-measure weighted mean analysis on passenger_count so I can see where ridership is spiking relative to service capacity.”
Multi-region transit performance report
“I need to compare ridership fluctuations across three transit regions for the last month. Can you take these 120 ridership rows and compute adjacent row deltas plus the weighted mean using service_count as the weight, focusing on the service_count field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular transport ridership data (up to 200 rows) and need both adjacent row-level change detection and a weighted mean in a single call for $0.01 USDC. It is best for offline/supplied-data analysis where no live data lookup, external forecasting, or prediction is needed. Prefer this over general analytics services when the dataset is already structured with row_id, region, date, passenger_count, and service_count fields and you want two specific statistical outputs bundled together.
Side effects
- Missing required fields (row_id, region, date, passenger_count, service_count) causes validation error
- Supplying more than 200 rows exceeds the row limit and returns an error
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Invalid field enum value (not passenger_count or service_count) causes rejection
- Empty rows array returns an error due to minItems:1 constraint
- Non-numeric values in passenger_count or service_count cause type errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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