This dataset contains lines for all highways in the state of New Mexico. It is in a vector digital data structure digitized from a USGS 1:500,000 scale map of the state of New Mexico to which highways: Interstate, U.S., and State have been added. The source was ARC/INFO 5.0.1. and the conversion software was ARC/INFO 7.0.3. The size of the file is .36 Mb, compressed.

This data set contains a 1:100,000 scale vector digital representation of all interstate highways, all US highways, most of the state highways, and some county roads in New Mexico. The data were collected using Trimble Pathfinder Basic Plus GPS units and differentially corrected with Trimble Pfinder software, version 2.40-07. They were converted to ARC/INFO format using ARC/INFO 7.0.3. The file size is approximately 4.2 Mb, compressed.

This data was downloaded from OpenStreetMap (OSM) roads data for Wisconsin from the OpenStreetMap's GeoFabrik website: http://www.geofabrik.de/data/download.html and reprojected to WTM 83/91. Several attributes were added to facilitate use of the OSM data in DNR basemaps. DNR has made edits to this data to correct errors where known and to hide road features within DNR Managed Lands that are not public roadways.This dataset contains only Interstate Highway, US Highways, and State Highways.To report errors in this dataset, contact Bill Ceelen at William.Ceelen@wisconsin.gov. Additional information about OSM is available on the GeoFabrik site: http://www.geofabrik.de/geofabrik/openstreetmap.html

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads.

NHS as officially accepted by the Council of Ministers, mapping by Transport Canada.

The North American Roads dataset was compiled on October 27, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset contains geospatial information regarding major roadways in North America. On March 31, 2025, the errant records with a value of 2 in the "NHS" field were corrected to have a value of 7 (Other NHS). The data set covers the 48 contiguous United States plus the District of Columbia, Alaska, Hawaii, Canada and Mexico. The nominal scale of the data set is 1:100,000. The data within the North American Roads layer is a compilation of data from Natural Resources Canada, USDOT’s Federal Highway Administration, and the Mexican Transportation Institute. North American Roads is a digital single-line representation of major roads and highways for Canada, the United States, and Mexico with consistent definitions by road class, jurisdiction, lane counts, speed limits and surface type.

The National Highway System (NHS) dataset and its geometries was updated on March 27, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Highway System consists of roadways important to the nation’s economy, defense, and mobility. The National Highway System (NHS) includes the following subsystems of roadways: Interstate - The Eisenhower Interstate System of highways, Other Principal Arterials - highways in rural and urban areas which provide access between an arterial and a major port, airport, public transportation facility, or other intermodal transportation facility, Strategic Highway Network (STRAHNET) - a network of highways which are important to the United States’ strategic defense policy and which provide defense access, continuity and emergency capabilities for defense purposes, Major Strategic Highway Network Connectors - highways which provide access between major military installations and highways which are part of the Strategic Highway Network, Intermodal Connectors - highways providing access between major intermodal facilities and the other four subsystems making up the National Highway System. A specific highway route may be on more than one subsystem. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529838

The National Highway Planning Network (NHPN) dataset was compiled on May 01, 2014 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset is a comprehensive network database of the nation's major highway system. It consists of the nation's highways comprised of Rural Arterials, Urban Principal Arterials and all National Highway System routes. The data set covers the 48 contiguous States plus the District of Columbia, Alaska, Hawaii, and Puerto Rico. The nominal scale of the data set is 1:100,000 with a maximal positional error of 80 meters.

This data set shows the centerlines of all public and some private roads within the state of Minnesota. Segments of pavement may have only one route or muitple routes traveling over them. One route will always be idenflied as primary and all attributes of these roadways signed to it. Other routes sharing the same pavement will be consisted co-incident or secendary and will not be a signed roadway attributes.

State highways are divided into segments called control sections for record keeping, maintenance, construction, and other administrative purposes. The four-digit control section number is composed of the two-number county code and an identifying two-digit number within that county. Control sections are revised due to jurisdictional transfers (typically from state to county) when new highway segments or entirely new state highways are built.

Routes State AID represent road centerlines for all state aid routes within the state of Minnesota.

Check other metadata records in this package for more information on routes centerlines.


Links to ESRI Feature Services:

Coincident Routes in Minnesota: Coincident Routes

MnDOT Control Sections: MnDOT Control Sections

MnDOT Roadway Routes in Minnesota: MnDOT Roadway Routes

Primary Routes in Minnesota: Primary Routes

State Aid Routes in Minnesota: State Aid Routes

Trunk Highways in Minnesota: Trunk Highways

Highways Statistics: Financial Data: Investment: Paraná data was reported at 983,425,000.000 BRL in 2017. This records a decrease from the previous number of 1,072,639,000.000 BRL for 2016. Highways Statistics: Financial Data: Investment: Paraná data is updated yearly, averaging 246,519,000.000 BRL from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 1,072,639,000.000 BRL in 2016 and a record low of 21,482,000.000 BRL in 1999. Highways Statistics: Financial Data: Investment: Paraná data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW004: Highways Statistics: Financial Data. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

This dataset comprises centerline segments for roads (both active and inactive, public and private, constructed or of record) in San Diego County based on data received from all official jurisdictions within the County (the County and 18 cities).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads.

Highways Statistics: Financial Data: Financial Expenses: Rio Grande do Sul data was reported at 8,862,000.000 BRL in 2013. This records a decrease from the previous number of 26,249,000.000 BRL for 2012. Highways Statistics: Financial Data: Financial Expenses: Rio Grande do Sul data is updated yearly, averaging 22,578,000.000 BRL from Dec 1998 (Median) to 2013, with 16 observations. The data reached an all-time high of 39,705,000.000 BRL in 2006 and a record low of 0.000 BRL in 1998. Highways Statistics: Financial Data: Financial Expenses: Rio Grande do Sul data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW004: Highways Statistics: Financial Data. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structure ...

Last Update: 08/29/2024The statewide roads dataset is a multi-purpose statewide roads dataset for cartography and range based-address location. This dataset is also used as the base geometry for deriving the GIS-representation of UDOT's highway linear referencing system (LRS). A network analysis dataset for route-finding can also be derived from this dataset. This dataset utilizes a data model based on Next-Generation 911 standards and the Federal Highway Administration's All Roads Network Of Linear-referenced Data (ARNOLD) reporting requirements for state DOTs. UGRC adopted this data model on September 13th, 2017.The statewide roads dataset is maintained by UGRC in partnership with local governments, the Utah 911 Committee, and UDOT. This dataset is updated monthly with Davis, Salt Lake, Utah, Washington and Weber represented every month, along with additional counties based on an annual update schedule. UGRC obtains the data from the authoritative data source (typically county agencies), projects the data and attributes into the current data model, spatially assigns polygon-based fields based on the appropriate SGID boundary, and then standardizes the attribute values to ensure statewide consistency. UGRC also generates a UNIQUE_ID field based on the segment's location in the US National Grid, with the street name then tacked on. The UNIQUE_ID field is static and is UGRC's current, ad hoc solution to a persistent global id. More information about the data model can be found here: https://docs.google.com/spreadsheets/d/1jQ_JuRIEtzxj60F0FAGmdu5JrFpfYBbSt3YzzCjxpfI/edit#gid=811360546 More information about the data model transition can be found here: https://gis.utah.gov/major-updates-coming-to-roads-data-model/We are currently working with US Forest Service to improve the Forest Service roads in this dataset, however, for the most up-to-date and complete set of USFS roads, please visit their data portal where you can download the "National Forest System Roads" dataset.More information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/transportation/roads-system/

U.S. Major Highways represents the major highways of the United States. These include interstates, U.S. highways, state highways, and major roads. This dataset is from the Census 2000 TIGER/Line files. It contains all Class 1 and 2 roads segments plus any other road segments necessary to provide network connectivity for the Class_Rte field.

The Highway Performance Monitoring System (HPMS) report is a national level report required to submit by each state to the Federal Highway Administration (FHWA) each year. The FHWA requires this as a responsibility to maintain data and system performance for roads and highways in the United States and its territories. The HPMS is the way FHWA can record this data as well as portion federal funding to states for transportation needs. To learn more about HPMS, refer to the ADOT HPMS Overview Storymap.This file includes data that have been submitted for the 2023 HPMS report, which represents all Arizona roads that were active during calendar year 2023. This file will not be updated. Future updates are uploaded to AZGeo/ArcGIS Online in separate feature services, titled by date and are typically uploaded in Fall/Winter each year.If you need assistance, please contact the Arizona Department of Transportation GIS team at MPDGIS@azdot.gov.

A compilation of historic documents and articles on the Interstate System, Federal-Aid Highway Program, FHWA, and transportation.