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Road Segment Observations — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Road Segment Observations — Record Quality Audit + String Normalisation + Secondary-Measure 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 2026-11-02).
Runs three local analytical reports on road segment observation data: a record quality audit, string normalisation, and a secondary-measure weighted mean — all in one call for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Road segment observations — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied road segment observations records (length_m, travel_seconds). 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 analytical reports returned in a single response: (1) a record quality audit flagging missing, malformed, or out-of-range values per row; (2) normalised string representations of text fields such as station_group; (3) a secondary-measure weighted mean computed over the chosen numeric field (length_m or travel_seconds) across all submitted rows.
Example use cases
Traffic data quality check
“I've got a CSV of road segment observations from last month — each row has a station group, date, road length in metres, and travel time in seconds. Can you audit the record quality, normalise the station group strings, and compute a weighted mean on travel_seconds for me across all 180 rows?”
Standardising station group labels
“Our road sensor dataset has inconsistent station group names like 'Grp-A', 'group_a', 'GROUP A' across 150 rows. Can you normalise those strings, run a quality audit on the records, and give me a weighted mean on the length_m field?”
Pre-processing road data for reporting
“Before I import these 95 road observation rows into our dashboard, can you run a quality audit and string normalisation pass, and also calculate the weighted mean on travel_seconds so I can spot any anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-15-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to batch-process road segment observation records locally without any external lookups or live data — specifically when you need all three of a quality audit, string normalisation, and a weighted mean in a single low-cost call. Ideal for pre-processing transport data pipelines before loading into dashboards or databases, especially when working with up to 200 rows of length_m and travel_seconds data.
Side effects
- Rows array empty or missing — validation error returned
- More than 200 rows submitted — request rejected with max-items error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- field enum value not 'length_m' or 'travel_seconds' — rejected
- Null numeric values may be excluded from weighted mean computation
- row_id missing or exceeding 80 characters — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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