Transportation

Overview

The Overture transportation theme is the collection of LineString and Point features that describe the infrastructure and conventions of how people and objects travel around the world. The dataset contains two features types: connector and segment. The three subtypes within segment -- road, rail, and water -- contain familiar categories of transportation data: highways, footways, cycleways, railways, ferry routes, and public transportation.

Most of the data in the transportation theme is sourced from OpenStreetMap. In the 2024-09-18.0 release, we began adding data from TomTom to improve coverage in key areas.

You might use the Overture transportation data for:

• mapping: rendering a map of connected roads and paths.
• routing: calculating optimal routes from place to place.
• navigation: generating granular instructions on the maneuvers needed to follow a route.
• analytics: transportation-related analysis including traffic safety analysis and disaster planning.
• geocoding: getting the coordinates of street intersections (geocodes) or the street intersection near specific coordinates (reverse geocodes).

This guide is an overview of the transportation data. To dig into the details of the schema, see the schema concepts for transportation and the reference documentation for the segment and connector feature types.

Dataset description

All Overture data, including transportation data, is distributed as GeoParquet, a column-based data structure. Below you'll find a table with column-by-column descriptions of the properties in the transportation feature type.

Schema for GeoParquet files in the transportation theme

segment

column	type	description
id	string	A feature ID. This may be an ID associated with the Global Entity Reference System (GERS) if—and-only-if the feature represents an entity that is part of GERS.
geometry	binary	The line representation of the segment's location. Segment's geometry which MUST be a LineSting as defined by GeoJSON schema.
bbox	struct	Area defined by two longitudes and two latitudes: latitude is a decimal number between -90.0 and 90.0; longitude is a decimal number between -180.0 and 180.0.
version	integer	Version number of the feature, incremented in each Overture release where the geometry or attributes of this feature changed.
sources	struct	The array of source information for the properties of a given feature, with each entry being a source object which lists the property in JSON Pointer notation and the dataset that specific value came from. All features must have a root level source which is the default source if a specific property's source is not specified.
subtype	string	The broad category of transportation segment.
class	string	Captures the kind of road and its position in the road network hierarchy.
names	struct	Properties defining the names of a feature.
connectors	struct	Array of connector IDs identifying the connectors this segment is physically connected to linearly referenced with their location. Each connector is a possible routing decision point, meaning it defines a place along the segment in which there is possibility to transition to other segments which share the same connector.
routes	struct	Routes this segment belongs to.
subclass_rules	struct	Defines the portion of a road that the subclass applies to.
access_restrictions	struct	Rules governing access to this road segment or lane.
level_rules	struct	Defines the Z-order, i.e. stacking order, of the road segment.
destinations	struct	Describes objects that can be reached by following a transportation segment in the same way those objects are described on signposts or ground writing that a traveller following the segment would observe in the real world. This allows navigation systems to refer to signs and observable writing that a traveller actually sees.
prohibited_transitions	struct	Defines where traveling from the segment to another is disallowed for navigation. This covers things situations prohibited turns or a transition from road to bike lane disallowing cars.
road_surface	struct	Defines the surface material on a road such as paved, asphalt, or unpaved.
road_flags	struct	Additional properties relevant to roads such as is_bridge or is_under_construction.
speed_limits	struct	Defines the speed limit of the road segment.
width_rules	struct	Defines the width of the road segment for rendering.
subclass	string	Specifies the usage of a length of road.
rail_flags	struct	Additional properties relevant to rail such as is_tunnel or is_freight.
filename	string	Name of the file being queried.
theme	string	Name of the Overture theme being queried.
type	string	Name of the Overture feature type being queried.

connector

column	type	description
id	string	A feature ID. This may be an ID associated with the Global Entity Reference System (GERS) if—and-only-if the feature represents an entity that is part of GERS.
geometry	binary	The line representation of the segment's location. Segment's geometry which MUST be a LineSting as defined by GeoJSON schema.
bbox	struct	Area defined by two longitudes and two latitudes: latitude is a decimal number between -90.0 and 90.0; longitude is a decimal number between -180.0 and 180.0.
version	integer	Version number of the feature, incremented in each Overture release where the geometry or attributes of this feature changed.
sources	struct	The array of source information for the properties of a given feature, with each entry being a source object which lists the property in JSON Pointer notation and the dataset that specific value came from. All features must have a root level source which is the default source if a specific property's source is not specified.
filename	string	Name of the file being queried.
theme	string	Name of the Overture theme being queried.
type	string	Name of the Overture feature type being queried.

Subtypes

Transportation segments are divided into three subtypes: rail, water, and road. The road subtype is then further divided into a variety of different classes based on usage captured in the table below.

Class and subclass feature counts

classes

subtype	class	subclass	feature count, August 2025
rail	funicular		1,309
rail	light_rail		16,218
rail	monorail		3,023
rail	narrow_gauge		31,044
rail	standard_gauge		1,429,962
rail	subway		52,417
rail	tram		58,992
rail	unknown		376,475
road	bridleway		99,768
road	cycleway	cycle_crossing	57,084
road	cycleway		1,272,514
road	footway	crosswalk	2,189,913
road	footway	sidewalk	3,532,386
road	footway		15,891,996
road	living_street		3,322,367
road	motorway	link	640,036
road	motorway		433,368
road	path		13,455,216
road	pedestrian		464,602
road	primary	link	490,259
road	primary		6,681,557
road	residential		125,698,481
road	secondary	link	388,309
road	secondary		10,716,541
road	service	alley	1,700,121
road	service	driveway	16,639,987
road	service	parking_aisle	6,322,008
road	service		32,980,815
road	steps		1,863,964
road	tertiary	link	297,428
road	tertiary		19,828,207
road	track		25,187,115
road	trunk	link	552,618
road	trunk		3,443,606
road	unclassified		29,598,013
road	unknown		1,279,752
water			28,259

Data access and retrieval

Overture's transportation theme data is freely available on both Amazon S3 and Microsoft Azure Blob Storage at these locations:

Segment

provider	location
Amazon S3	s3://overturemaps-us-west-2/release/2025-08-20.0/theme=transportation/type=segment/*
Azure Blob Storage	https://overturemapswestus2.blob.core.windows.net/release/2025-08-20.0/theme=transportation/type=segment/*

Connector

provider	location
Amazon S3	s3://overturemaps-us-west-2/release/2025-08-20.0/theme=transportation/type=connector/*
Azure Blob Storage	https://overturemapswestus2.blob.core.windows.net/release/2025-08-20.0/theme=transportation/type=connector/*

Data usage guidelines

We recommend downloading only the Overture data you need. If you have a particular geographic area of interest, there are several options for using a simple bounding box to extract places data and output a GeoJSON file.

Python Command-line Tool

First, follow the setup guide for the Python Command-line Tool.

Set type to either segment or connector and simply alter the bbox value to download a particular area.

overturemaps download --bbox=12.46,41.89,12.48,41.91 -f geojson --type=segment -o rome_segments.geojson

DuckDB

First, follow the setup guide for DuckDB.

Set the parquet link to either the connector or segment url depending on your needs.

Replace the bbox.xmin and bbox.ymin values with a new bounding box to run the query for a different area.

LOAD spatial; -- noqa
LOAD httpfs;  -- noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

COPY (
SELECT
    *
FROM read_parquet('s3://overturemaps-us-west-2/release/2025-08-20.0/theme=transportation/type=segment/*')
WHERE
    bbox.xmin > 12.46 AND bbox.xmax < 12.48 AND
    bbox.ymin > 41.89 AND bbox.ymax < 41.91
)
TO 'rome_segments.parquet';

Data manipulation and analysis

Querying by properties in DuckDB

These examples use data properties to filter the data in useful ways using DuckDB.

Query by class

The class column can be used to pull out subsets of the road data. Similarly, you could use subtype to select only water, rail, or road features. This example extracts only the parking_aisle features within the bounding box.

LOAD spatial; -- noqa
LOAD httpfs;  -- noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

COPY (
SELECT
    *
FROM read_parquet('s3://overturemaps-us-west-2/release/2025-08-20.0/theme=transportation/type=segment/*')
WHERE
    class = 'parking_aisle' AND
    bbox.xmin > 13.0897 AND bbox.xmax < 13.6976 AND
    bbox.ymin > 52.3100 AND bbox.ymax < 52.7086
)
TO 'berlin_parking_aisles.parquet';

Query by routes

You might be interested in a network of roads, such as a US Interstate. These can be extracted using the routes column and either using the network and ref properties and/or the wikidata column to identify the route.

This example extracts all the roads that are part of US I-5. To get all US Interstates simply remove AND routes[1].ref = '5' from the query.

LOAD spatial; -- noqa
LOAD httpfs;  -- noqa
-- Access the data on AWS in this example
SET s3_region='us-west-2';

COPY (
SELECT
    *
FROM read_parquet('s3://overturemaps-us-west-2/release/2025-08-20.0/theme=transportation/type=segment/*')
WHERE
    routes[1].network = 'US:I'
    AND routes[1].ref = '5'
)
TO 'US_I_5.parquet';

Querying by properties in Athena

Athena can allow for faster querying of the transportation layer than DuckDB given the size of the data. These examples are designed for Athena, but could be reworked for DuckDB with some tweaking.

Query by speed limit

To properly return a linear referenced feature like a speed limit, we will need to query all the possible values of the feature as the queried value may only exist on one portion of the line. In this example, we're extracting roads with any speed limit max_speed value of 27 and unit of mph using the any_match function.

This same general query would also work for querying other similar columns such as prohibited_transitions and access_restrictions.

SELECT
    id,
    speed_limits,
    ST_GEOMFROMBINARY(geometry) AS geometry
FROM v2025_08_20_0
WHERE type = 'segment'
    AND ANY_MATCH(
        speed_limits,
        speed_limit->speed_limit.max_speed.value = 27
        AND speed_limit.max_speed.unit = 'mph'
	)

Select connecting segments

With the connectors column it is simple to query for all features that connect with a particular segment without the need for a spatial query.

This example selects all the segments that that connect to the example id.

WITH input AS (
	SELECT id AS input_id,
		connector_id
	FROM v2025_08_20_0
		CROSS JOIN UNNEST(connectors) AS t(connector)
	WHERE type = 'segment'
		AND id = '08628d5437ffffff0473ffc36df547db'
)
SELECT
    *,
	ST_GEOMFROMBINARY(geometry) AS geometry
FROM v2025_08_20_0,
	input
WHERE type = 'segment'
	AND id != input_id
	AND ANY_MATCH(
		connectors,
		connector->connector.connector_id = input.connector_id
	)

Tools and libraries

transportation-splitter

Conceptual diagram of the splitter tool output. The numbers following 1234@ represent start_lr and end_lr values.

The transportation-spitter tool transforms Overture road data into simpler sub segments. It will optionally divide features at each connector point and at each change of a scoped based property, depending on configuration. Depending on your needs and map stack, the resulting dataset may be easier to manipulate than the original Overture data as each segment will only have connections at either end and have one set of properties for its entire length.

Since a GERS ID will no longer be unique with this output, the resulting data will have two additional columns: start_lr and end_lr which are linear references describing which section of the orginal feature this new segment comes from.

Splitter Example

To help visualize this process better, here is a real world example of a residential street in OpenStreetMap, Overture, and after being run through the splitter tool.

OpenStreetMap

In OpenStreetMap this residential road is represented by two different features with the same tags with feature 1 having an additional restricted access tag.

Overture

In Overture the two segments have been combined into one feature and the restricted access tag has been stored as this linear reference in access_restrictions:
[{'access_type': allowed, 'when': {'during': , 'heading': , 'using': , 'recognized': [as_private], 'mode': , 'vehicle': }, 'between': [0.521962729, 1.0]}]

Transportation Splitter

The splitter has sliced the Overture feature at each connector point for the driveways as well as at the point where the access restriction begins. This results in six unique features in the output all still sharing the same GERS ID.

More Information and Feedback

The tool requires a Spark environment to run and has been tested using Azure Databricks and AWS Glue. For set up information the transportation-spitter GitHub will contain the most up to date information as the tool is in active development still.

If you have feedback, questions, etc. on the tool you can create an issue on the GitHub.