> ## Documentation Index
> Fetch the complete documentation index at: https://docs.idoptlab.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Install airTravelModel and make your first prediction in under five minutes

## Install

```bash theme={null}
git clone https://github.com/Idopt-Lab/airTravelModel.git
cd airTravelModel
python -m venv .venv
source .venv/bin/activate
pip install -e ".[test]"
```

Requires Python 3.10+.

## Your first prediction

```python theme={null}
from air_travel_model import TransportPredictor

tp = TransportPredictor()

# Single mode — air fare, Q1 economy
fare = tp.predict("air_fare", "CHS", "ROA", quarter=1, is_business=False)
print(fare)
# {"price": 172.50, "currency": "USD"}

# All modes at once
results = tp.predict_all("CHS", "ROA", quarter=1)
for mode, result in results.items():
    print(f"{mode}: {result}")
```

## Specifying locations

Locations can be provided three ways — all are equivalent:

```python theme={null}
# IATA airport code
tp.predict("air_fare", "CHS", "JFK", quarter=2)

# City name (resolved to nearest airport)
tp.predict("air_fare", "Charleston, SC", "New York, NY", quarter=2)

# Lat/lon tuple
tp.predict("air_fare", (32.8986, -80.0406), (40.6413, -73.7781), quarter=2)
```

For train time, use Amtrak station codes for best accuracy:

```python theme={null}
tp.predict("train_time", "CHS", "NYP", quarter=2)   # NYP = New York Penn
tp.predict("train_time", "WAS", "CHI", quarter=3)   # WAS = Washington, CHI = Chicago
```

## Heuristic fallback

When a route has no training data, use `estimate()` instead of `predict()`:

```python theme={null}
# estimate() always returns a value — never raises
result = tp.estimate("drive_time", "BFD", "UNV")
```

`predict_all()` and `estimate_all()` call all seven models in one shot.

## Run the example script

```bash theme={null}
python scripts/example.py
```

This demonstrates all modes on a Charleston SC → Roanoke VA route with annotated output.