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_name	column_type	Description
id	VARCHAR	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	WKB	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	VARCHAR	The broad category of transportation segment.
class	VARCHAR	Captures the kind of road and its position in the road network hierarchy.
subclass	VARCHAR	Specifies the usage of a length of road.
subclass_rules	STRUCT[]	Defines the portion of a road that the subclass applies to.
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.
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.
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.
destinations	STRUCT[]	Describes the transitions from one segment to another on the way to a specified location. This data is primarily used for routing.

connector

column_name	column_type	Description
id	VARCHAR	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	BLOB	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.

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, November 2024 release
rail			1,572,582
road	bridleway		95,598
road	cycleway	cycle_crossing	47,189
road	cycleway		1,198,943
road	footway	crosswalk	1,839,806
road	footway	sidewalk	2,961,562
road	footway		14,555,896
road	living_street		3,053,898
road	motorway	link	626,752
road	motorway		428,359
road	path		12,727,710
road	pedestrian		448,737
road	primary	link	470,293
road	primary		6,511,508
road	residential		123,774,708
road	secondary	link	369,680
road	secondary		10,438,045
road	service	alley	1,541,197
road	service	driveway	15,160,138
road	service	parking_aisle	5,978,042
road	service		31,689,642
road	steps		1,691,848
road	tertiary	link	283,157
road	tertiary		19,199,059
road	track		23,956,033
road	trunk	link	535,332
road	trunk		3,342,592
road	unclassified		28,763,594
road	unknown		546,181
water			27,337

Data access and retrieval

The latest transportation data can be obtained from AWS or Azure as GeoParquet files at the following locations.

Segment

Provider	Location
Amazon S3	s3://overturemaps-us-west-2/release/2024-12-18.0/theme=transportation/type=segment/
Azure Blob Storage	https://overturemapswestus2.blob.core.windows.net/release/2024-12-18.0/theme=transportation/type=segment/

Connector

Provider	Location
Amazon S3	s3://overturemaps-us-west-2/release/2024-12-18.0/theme=transportation/type=connector/
Azure Blob Storage	https://overturemapswestus2.blob.core.windows.net/release/2024-12-18.0/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/2024-12-18.0/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/2024-12-18.0/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/2024-12-18.0/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 v2024_12_18_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 v2024_12_18_0
		CROSS JOIN UNNEST(connectors) AS t(connector)
	WHERE type = 'segment'
		AND id = '08628d5437ffffff0473ffc36df547db'
)
SELECT
    *,
	ST_GEOMFROMBINARY(geometry) AS geometry
FROM __ATHENA_OVERTURE_RELEASE,
	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': NULL, 'heading': NULL, 'using': NULL, 'recognized': [as_private], 'mode': NULL, 'vehicle': NULL}, '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.