# Transport Ridership Analytics: Adjacent Row Changes + Weighted Mean

> Transport Ridership Analytics: Adjacent Row Changes + 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 2026-10-28).

Computes adjacent row-over-row changes and a secondary-measure weighted mean across up to 200 transport ridership records (passenger_count, service_count), returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-05-159?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transport-ridership-analytics-adjacent-row-changes-weighted-mean-1d797510
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_bfdp4qxLI3ctKZRWdpRaW

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-analytics-adjacent-row-changes-weighted-mean-1d797510 -d '<json body>'
```

Example prompt: Analyze these 4 rows of transit ridership data — row-1 on 2026-10-01 region group-a passenger_count 10 service_count 1, row-2 on 2026-10-02 region group-b passenger_count 20 service_count 2, row-3 on 2026-10-03 region group-a passenger_count 15 service_count 3, row-4 on 2026-10-04 region group-b passenger_count 30 service_count 4 — give me the adjacent row changes and service-count weighted mean for passenger_count.

## When to prefer this

Choose this endpoint when you have tabular transport ridership data (up to 200 rows) and need both adjacent row-level change detection and a weighted mean in a single call for $0.01 USDC. It is best for offline/supplied-data analysis where no live data lookup, external forecasting, or prediction is needed. Prefer this over general analytics services when the dataset is already structured with row_id, region, date, passenger_count, and service_count fields and you want two specific statistical outputs bundled together.

## Known failure modes

- Missing required fields (row_id, region, date, passenger_count, service_count) causes validation error
- Supplying more than 200 rows exceeds the row limit and returns an error
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Invalid field enum value (not passenger_count or service_count) causes rejection
- Empty rows array returns an error due to minItems:1 constraint
- Non-numeric values in passenger_count or service_count cause type errors

## How this service works

Transport ridership — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + 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.

## Output

Returns two analytical reports: (1) adjacent row-over-row changes for each consecutive pair of supplied ridership records on the chosen field, and (2) a secondary-measure weighted mean of the chosen field (passenger_count or service_count) weighted by the other measure — all computed locally on the supplied data with no external lookups.

## 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
  },
  "field": {
   "enum": [
    "passenger_count",
    "service_count"
   ],
   "type": "string",
   "default": "passenger_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transport-ridership-analytics-adjacent-row-changes-weighted-mean-1d797510/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)