# US Origin-Airport Traffic Benchmarks — Monthly, Per Record (`nexgensignal/us-origin-airport-traffic-records`) Actor

US DOT T-100 origin-airport summary (r495-tyji) as clean, per-record throughput benchmarks — departures, passengers, seats, load factor and domestic/international splits per origin airport per month. Public-domain, $0.05 per record.

- **URL**: https://apify.com/nexgensignal/us-origin-airport-traffic-records.md
- **Developed by:** [NexGen Signal](https://apify.com/nexgensignal) (community)
- **Categories:** Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $33.50 / 1,000 origin-airport records

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## US Origin-Airport Traffic Benchmarks — Monthly, Per Record

Turn the U.S. DOT T-100 origin-airport summary into clean, per-record throughput benchmarks — one row per origin airport per month, with departures, passengers, seats, load factor and domestic/international splits, ready to benchmark airport throughput across the U.S. network.

Each source row becomes **one clean, flat record** with numeric fields coerced to real numbers, a stable
source-native `record_id`, and provenance stamped on every row: source, resource id, the licence notice,
the required attribution, a UTC retrieval timestamp and an interpretation caveat.

### What one record represents

The source is **`r495-tyji`** — *AFF - T100 Segment Summary By Origin Airport* on the U.S. DOT open-data portal (data.bts.gov). Each record is **one origin airport for one reporting month**: total performed departures, enplaned passengers, available seats and load factor, per-flight and distance metrics, and domestic / outbound-international / inbound-international splits. The origin-airport id, code and name are named explicitly in the query because the source default view can hide them.

For each record you get a composite `record_id` built from the source-native key, the analytic columns
listed below (reproduced verbatim, numbers as numbers), and the provenance block. Columns include `origin_airport_id`/`origin_airport_code`/`origin_airport_name`/`origin_city_name`, `reporting_month`, `total_departures`, `total_passengers`, `total_seats`, `total_load_factor`, and the `domestic_*` and `international_*` summaries.

### Coverage and volume

The live table holds **131,739** origin-airport-month records across the U.S. airport network and the full history the DOT publishes — that is the record capacity of a full pull.

**Live count: 131,739 records — matches the Wave-3 index figure exactly.**

The Actor pages the source with keyless SODA `$query` requests ordered by the source-native key for a
stable total order, and stops as soon as your **Maximum records** cap is met. Crucially, the source default view can hide the origin-airport identifier columns; this Actor issues a full SoQL `$query` that names them explicitly, so every delivered record carries its airport (the raw default view may not).

### Licence and attribution

This is a **public-domain U.S. Government work** (17 U.S.C. §105) — free to use, redistribute and build on. The full notice travels on every record:

> U.S. DOT / Bureau of Transportation Statistics. Public-domain U.S. Government work (17 U.S.C. 105). Reproduced verbatim; no third-party content or logos.

The required attribution — `U.S. DOT / Bureau of Transportation Statistics` — travels on every record.

### Interpretation caveat

Monthly per-origin-airport T-100 throughput totals. Load factor is a percentage; payload, freight and mail are in pounds; distances are in statute miles. Figures describe the airport, not a specific flight or carrier.

Values are reproduced verbatim: the Actor never rescales, re-derives or editorialises a number.

### Person-data policy

There are no name, email, phone, personal-address or personal-identifier fields — this is airport-level operational data. A per-record assertion enforces the person-field allow-list at write time.

### Data quality and freshness

Numeric fields are coerced from the source's string encoding into real numbers (integers where whole,
floats otherwise); genuinely missing cells are delivered as `null`, never as zero. Text is passed
through verbatim. Every run re-reads the live source, so the data is as fresh as the portal itself, and
each record's `observed_at` stamp records exactly when the row was retrieved. Delivery order is fixed by
the source-native key, so a capped sample and a later full pull agree on their overlap and a repeated
run returns rows in the same order. The `RUN_RECEIPT` reports source rows scanned and records delivered
and charged for a per-run reconciliation.

### Provenance, licensing and compliance

Every run begins with a live source-preflight: the Actor reads the exact host's `robots.txt` at runtime
and refuses to proceed if the crawl policy disallows the data path. The gate result — URL, HTTP status,
byte length and a SHA-256 of the policy — is written to the run's `RUN_RECEIPT`, so each run carries its
own audit trail. The Actor identifies itself with a transparent, non-impersonating User-Agent and never
bypasses a block, solves a challenge, or fetches through a cache or mirror. When the door is genuinely
unavailable the run fails loudly and bills nothing.

### Inputs

- **Maximum records** (`maxRecords`) — hard cap on records delivered and billed. Raise it to pull the
  full set; lower it to sample cheaply. Records arrive in a stable, source-native order.

### Output

Records land in the Actor's default dataset and export as JSON, CSV, Excel or via the Apify API. A
tabular **overview view** surfaces the most useful columns for quick inspection while the full record
retains every selected field and provenance stamp.

### Fields in detail

The record leads with `record_id` — a stable composite key drawn from the source's own grain — followed
by the analytic columns described above and closed by a provenance block: `source`, `source_dataset`
(the Socrata resource id), `licence`, `attribution`, `caveat` and `observed_at`. Every one of those
provenance fields is present on every record, so a single row is self-describing: hand it to a colleague
or a downstream system and it carries its own origin, licence and retrieval time without reference back
to this page. Because delivery is ordered by the source-native key, the same record always carries the
same `record_id` across runs, which makes the dataset safe to diff, deduplicate, or upsert into a
warehouse. Nothing in the record is computed or inferred beyond the explicit count where one is stated —
every other value is the source's own, reproduced byte-for-byte.

### Sibling Actors

This Actor is one of three distinct air-transport jobs in the fleet: **`us-carrier-traffic-performance-records`** measures traffic by **carrier** (airline), **`airport-facility-records`** is a **facility directory**, and this Actor measures **origin-airport throughput** (month × airport). Three different grains, three different questions. This Actor also shares its engineering — the runtime robots gate, push-then-charge
billing and verbatim-value discipline — with the fleet's other public-data records Actors.

### Pricing

This Actor uses Apify's pay-per-event model: a flat **$0.05 per record** actually delivered to the
dataset, and nothing else — no monthly rental, no per-run base fee, no compute charge. Deliver 40
records and you pay $2.00; deliver 10,000 and you pay $500.00. Billing is wired *after* delivery — each
record is pushed first and only then does the per-record event fire — so a mid-run failure can only
ever under-charge you, never over-charge. Use **Maximum records** to cap spend precisely.

### Scaling and limits

Set **Maximum records** low to sample the leading slice cheaply, or high to pull the full set. The Actor
paginates server-side and delivers incrementally, so memory stays flat regardless of how many records
you request, and you are billed only for what is actually delivered. Because the source is a live public
API, extremely deep pagination is ultimately bounded by the source's own paging behaviour; for the vast
majority of uses — sampling, a full refresh, or a scheduled top-up — the default paging is more than
sufficient. Schedule the Actor on Apify to keep a downstream table current: each run re-reads the live
source and re-stamps `observed_at`, so a nightly or weekly run gives you a dated, reproducible snapshot.

### Typical uses

Benchmark airport throughput month by month; rank airports by departures, passengers or seats; separate domestic from international activity at an airport; track seasonality and recovery; or feed a network-planning, infrastructure or investor model with clean airport-month records.

### What this Actor does not do

It does not forecast or model, does not merge multiple source tables into one record, and it does not include the redundant geocode columns some source views expose. It
gives you faithful, analysis-ready records — with a provenance trail you can audit on every run.

# Actor input Schema

## `maxRecords` (type: `integer`):

Maximum records delivered and billed. You are billed only for records actually delivered. Raise it to pull the full set.

## Actor input object example

```json
{
  "maxRecords": 500
}
```

# Actor output Schema

## `results` (type: `string`):

The delivered US origin-airport traffic record.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "maxRecords": 500
};

// Run the Actor and wait for it to finish
const run = await client.actor("nexgensignal/us-origin-airport-traffic-records").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "maxRecords": 500 }

# Run the Actor and wait for it to finish
run = client.actor("nexgensignal/us-origin-airport-traffic-records").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "maxRecords": 500
}' |
apify call nexgensignal/us-origin-airport-traffic-records --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgensignal/us-origin-airport-traffic-records"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/UYBlIjEo74K62yOfB/builds/zHDbToIvXrafF7aWV/openapi.json