TransportPredictor

Import

from air_travel_model import TransportPredictor
tp = TransportPredictor()

On instantiation, TransportPredictor loads all available .pkl model files from src/air_travel_model/data/. Missing files are skipped — the predictor still works with whatever models are present.

Methods

`predict(model, origin, destination, **kwargs) -> float | None`

Runs the ML model for the given mode. Returns a float (time in minutes or cost in USD), or None if the model is not loaded. For drive_time, routes not in the training dataset fall back to a live OSRM query before returning None. See Routing Fallback.

fare     = tp.predict("air_fare",   "CHS", "ROA", quarter=2, is_business=False)
air_min  = tp.predict("air_time",   "CHS", "ROA", quarter=2)
drv_min  = tp.predict("drive_time", "CHS", "ROA")

Parameter	Type	Description
`model`	`str`	Model name — see table below
`origin`	`str \| tuple`	IATA code, `"City, ST"`, or `(lat, lon)`
`destination`	`str \| tuple`	Same as origin
`**kwargs`		Model-specific options — see table below

Raises ValueError for unrecognised location strings.

`estimate(model, origin, destination, **kwargs) -> float | None`

Heuristic estimate — intended as a guaranteed fallback when predict() returns None or the model is not loaded. For drive_time, estimate() also queries OSRM first and only falls back to the haversine formula if OSRM is unavailable.

result = tp.estimate("drive_time", "BFD", "UNV")   # always returns a value

`predict_all(origin, destination, **kwargs) -> dict`

Calls predict() for all loaded models and returns a {model_name: float} dict. Models that return None are omitted from the result.

results = tp.predict_all("CRW", "ATL", quarter=2)
# {"air_fare": 253.74, "air_time": 59.0, "drive_time": 595.0, "drive_fare": 58.49}

`estimate_all(origin, destination) -> dict`

Calls estimate() for all models. Returns a {model_name: float} dict, skipping any model whose estimate() raises or returns None.

`available -> list[str]`

Returns model names that have a trained .pkl loaded.

print(tp.available)
# ["air_fare", "air_time", "drive_time", "drive_fare", ...]

Model names and kwargs

Model name	Unit	Required kwargs	Optional kwargs
`air_fare`	USD	`quarter` (1–4)	`is_business` (bool, default `False`)
`air_time`	minutes	`quarter` (1–4)	—
`drive_time`	minutes	—	—
`drive_fare`	USD	—	`gas_price` ($/gal, default `3.50`), `mpg` (default `28`)
`bus_fare`	USD	`quarter` (1–4)	`is_premium` (bool, default `False`)
`bus_time`	minutes	`quarter` (1–4)	—
`train_time`	minutes	`quarter` (1–4)	—

Location resolution

Locations are resolved through air_travel_model.resolvers.location.resolve():

IATA code ("CHS") — direct lookup in airports.csv
City name ("Charleston, SC") — matched against city, state entries in airports.csv
(lat, lon) tuple — used directly for coordinate-based routing; no IATA code is assigned, so models that require a code for route-stats lookup (air_fare, air_time) will fall back to their heuristics

Raises ValueError with a descriptive message for unrecognised strings. For train_time, origins and destinations are additionally resolved against amtrak_stations.csv. Amtrak station codes ("NYP", "WAS", "CHI") give the best accuracy because the model was trained on station-to-station pairs.

Air Travel Model

Api

Guides

Import

Methods

`predict(model, origin, destination, **kwargs) -> float | None`

`estimate(model, origin, destination, **kwargs) -> float | None`

`predict_all(origin, destination, **kwargs) -> dict`

`estimate_all(origin, destination) -> dict`

`available -> list[str]`

Model names and kwargs

Location resolution

​Import

​Methods

​predict(model, origin, destination, **kwargs) -> float | None

​estimate(model, origin, destination, **kwargs) -> float | None

​predict_all(origin, destination, **kwargs) -> dict

​estimate_all(origin, destination) -> dict

​available -> list[str]

​Model names and kwargs

​Location resolution

Import

Methods

`predict(model, origin, destination, **kwargs) -> float | None`

`estimate(model, origin, destination, **kwargs) -> float | None`

`predict_all(origin, destination, **kwargs) -> dict`

`estimate_all(origin, destination) -> dict`

`available -> list[str]`

Model names and kwargs

Location resolution