United States Public Road Length: Paved: Urban data was reported at 340,656.000 Mile in 2016. This records an increase from the previous number of 339,085.000 Mile for 2015. United States Public Road Length: Paved: Urban data is updated yearly, averaging 272,263.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 340,656.000 Mile in 2016 and a record low of 234,716.000 Mile in 1992. United States Public Road Length: Paved: Urban data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s United States – Table US.TA001: Public Road and Street Length.

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 filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata 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 sometoll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads.

United States Public Road Length: Paved data was reported at 2,750,499.000 Mile in 2016. This records an increase from the previous number of 2,735,207.000 Mile for 2015. United States Public Road Length: Paved data is updated yearly, averaging 2,577,963.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 2,750,499.000 Mile in 2016 and a record low of 2,271,225.000 Mile in 1993. United States Public Road Length: Paved data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s United States – Table US.TA001: Public Road and Street Length.

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 ...

An annual snapshot of county road mileage, in both lane and centerline miles, aggregated by Year, County, Function Classification, Thru Lane Surface, and Truck Route Classification. Washington State counties are required to maintain a complete inventory of all county roads, assuring the capability to evaluate and compare the transportation needs and capabilities across the state, thus providing a high level of accountability both by individual county and statewide (WAC 136-60). Each county is responsible for maintaining current information regarding its Road Log and, no later than April 1st of each year, submits an updated Road Log for its complete road system with all data elements as of December 31st of the preceding year (known in this dataset as "Calendar Year"). This dataset contains aggregated mileage submitted by WA counties and certified by the WA State County Road Administration Board (CRAB) as part of the county road inventory. More information about the County Road Log is available at: https://www.crab.wa.gov/county-road-log.

United States US: Total Road Network: %: Urban Roads data was reported at 29.388 % in 2022. This records a decrease from the previous number of 29.436 % for 2021. United States US: Total Road Network: %: Urban Roads data is updated yearly, averaging 25.948 % from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 29.436 % in 2021 and a record low of 20.495 % in 1994. United States US: Total Road Network: %: Urban Roads data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Transport Infrastructure, Investment and Maintenance: OECD Member: Annual. [COVERAGE] The road network is all roads in a given area. LENGTH OF URBAN ROADS A road inside a built-up area, with entries and exits sign-posted as such. Motorways, express roads and other roads of higher speed traversing the built-up area, if not signed-posted as built-up roads are not included. Streets are included. LENGTH OF ROADS A road is a line of communication (travelled way) open to public traffic, primarily for the use of road motor vehicles, using a stabilised base other than rails or air strips. Paved roads and other roads with a stabilised base, e.g. gravel roads, are included. Roads also cover streets, bridges, tunnels, supporting structures, junctions, crossings and interchanges. Toll roads are also included. Dedicated cycle lanes are not included. [COVERAGE] Road refers to the US definition of either roadway or traffic way. Roadway (travelled portion of road) and shoulder, if an, make up the road. Trafficway is the entire right-of-way (or land way set outside) containing one or more roads for traffic in the same or opposite directions. [STAT_CONC_DEF] The length of the road is the distance between its start and end point. If one of the directions of the carriageway is longer than the other then the length is calculated as the sum of half of the distances of each direction of the carriageway from first entry point to last exit point.

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.

This CSV file contains geometric and topological road network statistics for the majority of counties in the conterminous U.S. The underlying road network data is the USGS-NTD v2019. These road network data from 2019 were clipped to historical settlement extents obtained from the HISDAC-US dataset Road network statistics are multi-temporal, calculated in time slices for the years: 1810-1900, 1880-1920, 1900-1940, 1920-1960, 1940-1980, 1960-2000, 1980-2015 The historical built-up areas used to model the historical road networks are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the modelling strategy. Spatial coverage: all U.S. counties that are covered by the HISDAC-US historical settlement layers. This datasets includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021

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.

Graph and download economic data for Vehicle Miles Traveled (TRFVOLUSM227NFWA) from Jan 1970 to Jul 2025 about miles, travel, vehicles, and USA.

United States Public Road Length: Unpaved data was reported at 1,362,044.000 Mile in 2016. This records a decrease from the previous number of 1,391,593.000 Mile for 2015. United States Public Road Length: Unpaved data is updated yearly, averaging 1,417,904.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 1,633,496.000 Mile in 1993 and a record low of 1,324,245.000 Mile in 2008. United States Public Road Length: Unpaved data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s United States – Table US.TA001: Public Road and Street Length.

The TIGER/Line Shapefiles are the fully-supported, core geographic products from the US Census Bureau. They are extracts of selected geographic and cartographic information from the US Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. The all roads dataset contains all linear street features with "S" (Street) type MTFCCs in the MAF/TIGER database. These include primary roads, secondary roads, local neighborhood roads, rural roads, city streets, vehicular trails (4WD), ramps, service drives, walkways, stairways, alleys, and private roads. The all roads dataset is published at two granularities: A single table for roads in all states and territories. A separate table for each state and territory. The tables follow the naming convention all_roads_[State FIPS Code]. State FIPS codes, and the corresponding state name, are available using the State FIPS code table in BigQuery and in the sample section below. For more details on each dataset, see the TIGER/Line technical documentation published by the Census Bureau. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Aug 2025 about miles, travel, vehicles, and USA.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.3072 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0337 and 0.0178 (in million kms), corressponding to 10.9845% and 5.798% respectively of the total road length in the dataset region. 0.2557 million km or 83.2175% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0017 million km of information (corressponding to 0.6463% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

United States Public Road Length: Paved: Rural data was reported at 611,652.000 Mile in 2016. This records an increase from the previous number of 609,080.000 Mile for 2015. United States Public Road Length: Paved: Rural data is updated yearly, averaging 646,737.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 660,659.000 Mile in 2001 and a record low of 580,744.000 Mile in 2011. United States Public Road Length: Paved: Rural data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s United States – Table US.TA001: Public Road and Street Length.

The Road Segment table describes the administration and ownership of the segment of road. It contains tabular polyline data showing the log miles/measures, road name, functional class, government control, and U.S. Routes. Road names are derived from visual surveys by field crew or official GIS maps. Functional class is set by the Federal Highway Administration (FHWA). All other categories are determined by state and local agencies. This dataset is updated weekly. County – County in Tennessee where associated features and attributes are located.Route Number – Route in Tennessee with corresponding attributes.Special Case – Route designator for non-standard routes such as By-Pass.00 None01 Spur - S02 Alternate - A03 State Connector - C04 Bypass - BP05 Business Route - BR06 Northbound - N07 Southbound - S08 Eastbound - E09 Westbound - WCounty Sequence – This number indicates the sequential number of times a route enters and leaves the county, begins with zero (0).Beginning Log Mile (BLM) – The beginning log mile (measure) for the route segment.Ending Log Mile (ELM) - The ending log mile (measure) for the route segment.Functional Classification – These codes, set by the FHWA, provide a statewide highway functional classification in rural and urban areas to determine functional usage of the existing roads and streets.01 Rural Interstate02 Rural Other Principal Arterial03 Rural Freeway or Expressway06 Rural Minor Arterial07 Rural Major Collector08 Rural Minor Collector09 Rural Local11 Urban Interstate12 Urban Freeway or Expressway14 Urban Other Principal Arterial16 Urban Minor Arterial17 Urban Collector19 Urban LocalGovernment Control – These codes determine ownership and maintenance responsibility.01 State Highway Agency02 County04 Municipal11 State Park12 Local Park21 Other State Agency25 Other Local Agency26 Private27 Railroad40 Other Public60 Other Federal Agency63 US Fish and Wildlife64 US Forest Service66 National Park Service67 TVA68 Bureau of Land Management70 Corps of Engineers (Civil)72 Air Force73 Navy or Marines74 Army80 OtherUS Route Number – US Route Number assigned to roadway segment.

This dataset represents the road density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. This data set is derived from TIGER/Line Files of roads in the conterminous United States. Road density describes how many kilometers of road exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. The (kilometer of road/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

We provide a roads dataset that includes the spatial location of roads, the estimated age of each road, and the predicted traffic volume of each road between 1986 and 2020 in Wyoming, USA. Our annual estimates of traffic volume are available for each road and include estimates for all vehicles and truck only traffic. Moreover, we provide the estimated age of each road, where a minimum value of 1986 indicates that the road existed in 1986, and any later year indicates the most likely year that road was developed. This dataset will be beneficial for any research focused on the mechanistic effects of road traffic on wildlife populations. Our roads dataset is based on a comprehensive inventory of paved and unpaved roads in Wyoming of 2015 National Aerial Imagery Program (NAIP) aerial imagery (Fancher et al. 2023). We developed annual estimates of road age and vehicular traffic volume across 147,108 km of highways, arterials, collectors, local, and gravel/graded roads within the state ...

This collection contains measures of primary and secondary roads (highways and main arteries) per United States census tract and per United States ZIP code tabulation area (ZCTA) in 2010 and 2020. These measures may be used as a proxy for heavy traffic, high traffic speeds, and impediments to walking or biking. Variables include: counts of primary, secondary, and all streets per tract and per ZCTA; total length of primary, secondary, and all streets per tract and per ZCTA; ratio of primary and/or secondary road counts to all roads; and ratio of length of primary/secondary roads to all streets.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.2963 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.3221 and 0.1183 (in million kms), corressponding to 24.8447% and 9.1242% respectively of the total road length in the dataset region. 0.856 million km or 66.031% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0104 million km of information (corressponding to 1.2101% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.