{"uid":"cap_iKW0Au6I1SdLod-DHiTxU","slug":"transport-ridership-string-normalisation-and-secondary-measure-1d3d2e12","name":"Transport Ridership String Normalisation and Secondary-Measure Weighted Mean","description":"Transport ridership — String normalisation + Secondary-measure weighted mean: String normalisation + 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-economics-05-034?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","passenger_count","service_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"service_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passenger_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","service_count":1,"passenger_count":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","service_count":2,"passenger_count":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["passenger_count","service_count"],"type":"string","default":"passenger_count"}}},"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_WBLl68kb-VtsT6ZYu0-b8","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":"Normalises region/date strings and computes a service_count-weighted mean of passenger_count (or vice versa) for up to 200 transport ridership rows, returning two analytical reports.","exampleAgentPrompt":"Take my transit ridership records — I have rows with region, date, passenger_count, and service_count — and run string normalisation on the region and date fields, then compute a service_count-weighted mean of passenger_count across all rows. I have about 50 rows covering routes in 'group-a' and 'group-b'.","exampleUseCases":[{"title":"Weekly ridership summary by region","prompt":"I have 120 rows of daily bus ridership data with region names like 'North-East Corridor' and 'downtown_west', passenger counts, and service counts. Can you normalise those region strings and give me the service-count weighted mean of passenger_count across all of them?"},{"title":"Transit agency data quality check","prompt":"Our transit dataset has inconsistent date formats and region labels across 80 records. Can you normalise the strings and compute the weighted average passenger count — weighted by service_count — so I can send a clean summary to the board?"},{"title":"Comparing service frequency vs ridership","prompt":"I want to understand how ridership correlates with service frequency. I have 200 rows of metro data with passenger_count and service_count. Please normalise the region and date strings, then compute the service_count-weighted mean of passenger_count for me."}],"resultDescription":"Two analytical reports covering: (1) normalised string output for region and date fields across all submitted rows, and (2) a secondary-measure (e.g. service_count) weighted mean of the selected primary field (e.g. passenger_count), computed locally without any external lookups or predictions.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, region, date, passenger_count, service_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","All service_count values are null or zero — weighted mean undefined","Payment of 0.01 USDC not provided — request blocked by x402 paywall"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of region/date labels AND a weighted-mean computation on transport ridership data in a single call, without requiring any live data lookup, external API, or prediction — especially useful for batch analytical pipelines over up to 200 rows of supplied transit records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T12:53:43.767Z","isFirstParty":false,"canonicalSlug":"transport-ridership-string-normalisation-and-secondary-measure-1d3d2e12"}