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Triplegs

Triplegs summarizes one movement segment between consecutive staypoints. Each row describes a door-to-door segment rather than its raw point geometry, making it suitable for mode classification and trip aggregation.

Required data

Validated triplegs contain tripleg_id, started_at, finished_at, length_km, and duration_s, plus a user ID when the data represents multiple people. Generated triplegs also include mean speed information.

classified = triplegs.predict_transport_mode()
trips = classified.generate_trips(active_staypoints)

generate_trips() requires staypoints with an activity flag. Create that flag first with Staypoints.create_activity_flag().

API

fastmob.core.triplegs_dataframe.Triplegs

Bases: BaseDataFrame

One row per tripleg (a movement segment between two staypoints).

.df is the per-tripleg summary table: tripleg_id, started_at, finished_at, length_km, duration_s, mean_speed_kmh, plus uid_col when present. Reconstructing each tripleg's point-by-point geometry (e.g. for a LineString) is not built in this pass.

Parameters:

Name Type Description Default
df DataFrame - like

Source data; any Narwhals-compatible eager backend.

 required
uid_col str

User-ID column name.

 None
validate bool

When True (default), check that required columns are present.

 True

calculate_modal_split(freq=None, metric='count', per_user=False, normalize=False)

Aggregate this mode-labeled table into a modal-split table.

See :func:fastmob.preprocessing.calculate_modal_split.

Returns:

Type Description
DataFrame

Modal split aggregated by the requested frequency and metric.

Examples:

>>> modal_split = classified_triplegs.calculate_modal_split(metric="count")

from_positionfixes(positionfixes, staypoints, gap_threshold_min=15.0, **stop_kwargs) staticmethod

Derive triplegs from positionfixes + already-generated staypoints.

See :meth:fastmob.core.positionfixes_dataframe.Positionfixes.generate_triplegs.

Returns:

Type Description
Triplegs

Movement segments derived from the raw fixes and stops.

Examples:

>>> triplegs = Triplegs.from_positionfixes(positionfixes, staypoints)

generate_trips(staypoints, gap_threshold_min=15.0)

Group consecutive triplegs into trips.

See :meth:fastmob.core.trips_dataframe.Trips.from_triplegs.

Examples:

>>> trips = triplegs.generate_trips(activity_staypoints)

predict_transport_mode(method='simple-coarse', categories=None)

Classify each tripleg's transport mode from its average speed.

See :func:fastmob.preprocessing.predict_transport_mode.

Parameters:

Name Type Description Default
method str

Classification method. Default is "simple-coarse".

 'simple-coarse'
categories dict or None

Custom speed-to-mode categories.

 None

Returns:

Type Description
Triplegs

Copy with a mode column.

Examples:

>>> classified = triplegs.predict_transport_mode()