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Road Segment Observations — String Normalisation + Date by Category Pivot
x402
Road Segment Observations — String Normalisation + Date by Category Pivot 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 string fields and produces a date-by-category pivot table for road segment observation records containing length_m and travel_seconds fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Road segment observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied road segment observations records (length_m, travel_seconds). 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 analytical reports: (1) a string normalisation report for the station_group field, cleaning and standardising string values across up to 200 rows; (2) a date-by-category pivot table for the selected numeric field (length_m or travel_seconds), cross-tabulating values by date and station group category.
Example use cases
Clean and pivot commute corridor data
“I've got a batch of road segment observations for our commute corridors — each row has a station group name, a date, length in metres, and travel seconds. Can you normalise the station group strings and give me a date-by-category pivot on travel_seconds so I can see how travel times vary by group over time?”
Audit road sensor data quality
“We collected road sensor readings over the past month with up to 150 rows, each tagged with a station group, date, and length_m. Please run string normalisation on the station group labels and pivot the length_m values by date and category so I can spot inconsistencies.”
Summarise road works impact by segment group
“Here are 180 road segment observations from our road works monitoring project — row IDs, station groups, dates, lengths and travel times included. Run string normalisation on the station groups and produce a date-by-category pivot for length_m so we can compare segment behaviour across groups before and after works.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-15-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have road segment observation data (with station groups, dates, and numeric measurements) and need both string normalisation of categorical labels and a date-by-category pivot in a single low-cost call. Ideal for offline batch analysis of up to 200 rows where no live lookup, prediction, or external data enrichment is needed.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, station_group, date, length_m, travel_seconds) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null numeric values may affect pivot completeness
- field enum value must be exactly 'length_m' or 'travel_seconds'
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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