osm-lts 0.3.0

Classify OpenStreetMap ways by Level of Traffic Stress (LTS) using the Furth methodology.

osm-lts

Classify OpenStreetMap ways by Level of Traffic Stress (LTS) using the Furth methodology.

LTS is a 1–4 scale from "kid-comfortable" (1) to "strong-and-fearless only" (4). It's the standard advocacy and planning input for "where is the bike network actually rideable for a typical adult" — far more honest than miles of "bike infrastructure" because it captures whether that infrastructure is on a calm street or a six-lane arterial.

Install

pip install osm-lts

Pure Python, no dependencies, Python 3.9+.

Use

from osm_lts import classify

classify({"highway": "residential", "maxspeed": "25 mph"})
# <LTS.MOST_ADULTS: 2>

classify({"highway": "primary"})
# <LTS.STRONG_AND_FEARLESS: 4>

classify({"highway": "cycleway"})
# <LTS.KID_COMFORTABLE: 1>

classify({"highway": "footway"})
# None — outside scope (not relevant to cyclist stress)

The function takes any Mapping[str, str] of OSM tags. Numeric tags (maxspeed, lanes) tolerate units ("25 mph", "50 km/h", "4;3") — only the leading digits are read. The result is an IntEnum, so int(classify(tags)) gives you the bare LTS value for serialization.

CLI

The package ships with an osm-lts command for batch jobs:

echo '{"highway": "residential", "maxspeed": "25 mph"}' | osm-lts classify
# {"tags": {"highway": "residential", "maxspeed": "25 mph"}, "lts": 2}

osm-lts classify --in ways.jsonl --out lts.jsonl

Input is JSON, JSONL, or a single JSON array — auto-detected.

How it works

The classifier mirrors Furth's published rules:

Tier	Description	Example triggers
LTS 1	Suitable for children	highway=cycleway, living_street, cycleway=track
LTS 2	Most adults will tolerate	residential ≤25 mph, bike lane on a slow street
LTS 3	Experienced cyclists only	tertiary, fast residential, bike lane on a faster street
LTS 4	Strong-and-fearless only	primary / trunk, >35 mph, ≥3 lanes and >30 mph

The branches evaluate top-to-bottom and short-circuit on the first match. Order matters — a cycleway=track on a 40 mph arterial returns LTS 1 because separation wins over speed. Highways outside scope (motorway, footway, sidewalk, steps, pedestrian) return None.

When maxspeed or lanes are missing, highway-typical defaults fill in:

from osm_lts import (
    DEFAULT_SPEED_MPH_BY_HIGHWAY,
    DEFAULT_LANE_COUNT_BY_HIGHWAY,
    EXCLUDED_HIGHWAYS,
)

These are public so callers can read them in their own UIs (e.g. "we assumed 25 mph because the way was untagged").

Customizing the rules

Wrap a Classifier instance to override any of the defaults. Useful for modeling a city or country whose posted-speed conventions or in-scope highway set differ from the US-centric defaults the package ships with.

from osm_lts import Classifier, EXCLUDED_HIGHWAYS

# Stricter unknown-speed default:
strict = Classifier(speed_mph_fallback=20)
strict({"highway": "residential"})  # <LTS.MOST_ADULTS: 2>

# Drop pedestrian-priority paths out of scope entirely:
narrower = Classifier(excluded_highways=EXCLUDED_HIGHWAYS | {"path"})
narrower({"highway": "path", "bicycle": "designated"})  # None

# Per-highway speed overrides:
slower_residential = Classifier(speed_mph_by_highway={"residential": 20})

Classifier is a frozen dataclass — instances are hashable and thread-safe to share. Use dataclasses.replace(clf, ...) for tweaked copies.

Origin

Extracted from the Bike Streets city-mapping platform.

Development

pip install -e '.[test]'
pytest

License

MIT. See LICENSE.