# Transport Ridership String Normalisation + Two-Measure Correlation

> Transport Ridership String Normalisation + Two-Measure Correlation 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 region/date strings and computes a two-measure Pearson/rank correlation between passenger_count and service_count for up to 200 transport ridership rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-05-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transport-ridership-string-normalisation-two-measure-correlation-19294384
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_rstcXRmsTvLfY63SZFnul

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability transport-ridership-string-normalisation-two-measure-correlation-19294384 -d '<json body>'
```

Example prompt: I have 150 rows of bus ridership data with region names that need cleaning — can you normalise the region and date strings and then compute a correlation between passenger_count and service_count across all of them?

## When to prefer this

Choose this endpoint when you have a local batch of up to 200 transport ridership records and need both string normalisation (cleaning region/date labels) and a correlation analysis between two numeric measures (passenger_count and service_count) in a single cheap call. It is ideal when you do not need live external data, predictions, or professional assessments — only local analytical computation on supplied data. Prefer it over generic statistics libraries when you want a bundled, pay-per-use API that handles both data cleaning and correlation in one step at a fixed low cost.

## Known failure modes

- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, region, date, passenger_count, or service_count) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- All passenger_count or service_count values are null — correlation cannot be computed
- Malformed JSON body — request rejected
- Payment not included or insufficient — x402 payment required error

## How this service works

Transport ridership — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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

Two structured analytical reports: (1) a string normalisation report showing standardised region and date values for each input row, and (2) a two-measure correlation report quantifying the statistical relationship between passenger_count and service_count across all supplied rows. No external data is fetched; all analysis is performed on the supplied records only.

## Request schema (JSON Schema)

```json
{
 "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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transport-ridership-string-normalisation-two-measure-correlation-19294384/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)