{"uid":"cap_SJ2E4qThqcuplj1dPPAP0","slug":"road-segment-observations-numeric-row-ordering-trailing-mean-series-f50ebd63","name":"Road Segment Observations — Numeric Row Ordering + Trailing Mean Series","description":"Road segment observations — Numeric row ordering + Trailing mean series: Numeric row ordering + Trailing mean series 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-15-115?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","length_m","travel_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"length_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"travel_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","length_m":10,"station_group":"group-a","travel_seconds":1},{"date":"2026-10-02","row_id":"row-2","length_m":20,"station_group":"group-b","travel_seconds":2},{"date":"2026-10-03","row_id":"row-3","length_m":15,"station_group":"group-a","travel_seconds":3},{"date":"2026-10-04","row_id":"row-4","length_m":30,"station_group":"group-b","travel_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["length_m","travel_seconds"],"type":"string","default":"length_m"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_zLveAXVGi1ssKBEur6_dt","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes numeric row ordering and trailing mean series for road segment observation records (length_m, travel_seconds) across up to 200 rows.","exampleAgentPrompt":"I have 4 road segment observations — row-1 (group-a, 2026-10-01, 10m, 1s), row-2 (group-b, 2026-10-02, 20m, 2s), row-3 (group-a, 2026-10-03, 15m, 3s), row-4 (group-b, 2026-10-04, 30m, 4s) — can you compute the numeric row ordering and trailing mean series for travel_seconds with a window of 2?","exampleUseCases":[{"title":"Smoothing daily travel time fluctuations","prompt":"I have 10 daily road segment records with travel_seconds values that fluctuate a lot — can you give me the numeric row ordering and a trailing mean with a window of 3 to smooth them out?"},{"title":"Ranking segments by road length","prompt":"Here are 50 road segment observations across two station groups — please compute the numeric row ordering and trailing mean series for the length_m field with a window of 5 so I can see the trend."},{"title":"Detecting trends in commute data","prompt":"I've collected 200 road segment entries over the past year with station_group, date, length_m, and travel_seconds — run numeric row ordering and trailing mean on travel_seconds with a window of 7 so I can spot any long-term patterns."}],"resultDescription":"Two analytical reports: (1) numeric row ordering of the submitted records and (2) a trailing mean series for the chosen field (length_m or travel_seconds) computed over the specified rolling window, both derived entirely from the supplied data with no external lookups.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — rejected, exceeds max","Window size larger than number of rows — may produce sparse or empty trailing mean series","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, date, length_m, travel_seconds) — request rejected","Non-numeric values in length_m or travel_seconds where numbers expected — schema error"],"whenToPreferThis":"Choose this endpoint when you have tabular road segment data with length or travel time fields and need both a ranked row ordering and a rolling/trailing mean series computed in one call. It is ideal for lightweight analytical preprocessing of up to 200 rows without requiring a full data pipeline. Prefer it over general-purpose analytics tools when you want a fast, low-cost ($0.01 USDC) dual-report computation on supplied data with no external dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T13:15:46.644Z","isFirstParty":false,"canonicalSlug":"road-segment-observations-numeric-row-ordering-trailing-mean-series-f50ebd63"}