osm-lts 0.4.0

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").

Scope and limitations

The classifier treats each OSM way in isolation — it reads that way's tags and returns a tier. That's the foundational input to a network LTS analysis, but it's not the whole methodology:

• No intersection effects. A calm residential that crosses a hostile arterial is still LTS 2 here; full Furth would penalize the unsignalized crossing.
• No adjacent-way context. Parking presence, buffer width, and sidewalk separation aren't read from neighboring ways. A bike lane next to a parking lane is scored the same as one without.
• No network-level analysis. The output is a per-way score, not "low-stress islands" or connectivity. Building a routing or coverage tool means doing that analysis on top of the per-way scores this library returns.

SQL form (PostgreSQL / osm2pgsql)

The same rules are also available as a PostgreSQL CASE expression via osm_lts.sql. The SQL emitter and the Python classifier share their constants — change a default in one place and both forms move together.

from osm_lts.sql import lts_case_expression

sql = f"""
    SELECT
        id,
        ({lts_case_expression()}) AS lts
    FROM planet_osm_ways
    WHERE tags ? 'highway'
"""

Defaults assume osm2pgsql slim mode with --hstore-all — tags live as hstore on planet_osm_ways.tags and are cast to jsonb for ->> extraction. Different schemas (osm2pgsql flex, imposm3, custom DDL) work via per-input keyword overrides:

lts_case_expression(
    tags_jsonb="pw.tags::jsonb",   # custom alias
    highway_sql="pol.highway",      # pre-extracted column
)

Classifier overrides flow into the SQL too — a city-specific tuning produces SQL with the new defaults baked in:

clf = Classifier(speed_mph_fallback=20)
lts_case_expression(classifier=clf)  # emits "ELSE 20" in the speed CASE

The submodule also exposes the building blocks individually (speed_mph_expression, lane_count_expression, cycleway_kind_expression, excluded_highways_in_list) for use in custom queries.

Recipes for different OSM database layouts

1. osm2pgsql slim + hstore (the default) — tags lives as hstore on planet_osm_ways.tags; no extra columns. The defaults work as-is:

from osm_lts.sql import lts_case_expression

sql = f"""
    SELECT id, ({lts_case_expression()}) AS lts
    FROM planet_osm_ways
    WHERE tags ? 'highway'
"""

2. osm2pgsql slim, classifying alongside planet_osm_line geometry — planet_osm_line materializes highway, name, etc. as columns, but the full tag bag is back on planet_osm_ways.tags. Use the cheap column where it's available, fall back to the tag bag for everything else:

sql = f"""
    SELECT
        pol.osm_id,
        pol.way AS geom,
        ({lts_case_expression(
            tags_jsonb="pw.tags::jsonb",
            highway_sql="pol.highway",
            maxspeed_sql="(pw.tags::jsonb)->>'maxspeed'",
        )}) AS lts
    FROM planet_osm_line pol
    JOIN planet_osm_ways pw ON pw.id = pol.osm_id
    WHERE pol.highway IS NOT NULL
"""

(Pre-filtering on pol.highway IS NOT NULL is much faster than post-filtering on the LTS NULL result.)

3. osm2pgsql flex output with native jsonb tags — flex Lua often defines a single table with tags jsonb (no hstore cast needed). Drop the ::jsonb and the rest works:

sql = f"""
    SELECT id, ({lts_case_expression(tags_jsonb="tags")}) AS lts
    FROM osm_ways  -- whatever your flex script named it
"""

4. imposm3 — different schema again. Tags live as hstore on osm_roads.tags, but most fields you care about are already broken out into columns. Use the materialized columns and convert the hstore to jsonb only for cycleway sub-tags:

sql = f"""
    SELECT
        osm_id,
        geometry,
        ({lts_case_expression(
            tags_jsonb="hstore_to_jsonb(tags)",  # for cycleway COALESCE
            highway_sql="type",                    # imposm3 names it 'type'
            maxspeed_sql="tags->'maxspeed'",       # raw hstore -> text
            bicycle_sql="tags->'bicycle'",
        )}) AS lts
    FROM osm_roads
"""

(tags->'k' is the hstore text accessor — analogous to JSON ->>.)

5. Custom schema with a pre-resolved cycleway column — if your ETL already collapsed cycleway / cycleway:right / cycleway:left / cycleway:both down to a single cycleway_kind column, skip the COALESCE entirely:

sql = f"""
    SELECT id, ({lts_case_expression(
        cycleway_kind_sql="cycleway_kind",  # plain column reference
    )}) AS lts
    FROM ways_with_resolved_cycleway
"""

6. Pre-filtering at the source — for tile servers and other hot paths, drop excluded highways at the row source so the CASE never sees them:

from osm_lts.sql import excluded_highways_in_list, lts_case_expression

sql = f"""
    SELECT id, ({lts_case_expression()}) AS lts
    FROM planet_osm_ways
    WHERE tags->'highway' IS NOT NULL
      AND tags->'highway' NOT IN ({excluded_highways_in_list()})
"""

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.