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.

In 2023, the highway network in the United States had a total length of around 4.2 million statute miles. One statute mile is approximately equal to 5,280 feet. The United States has one of the most extensive road networks worldwide.

Forecast: Paved Roads Length in the US 2024 - 2028 Discover more data with ReportLinker!

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.

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.

United States Public Road Length: Paved: Rural Local data was reported at 832,818.000 Mile in 2016. This records an increase from the previous number of 816,483.000 Mile for 2015. United States Public Road Length: Paved: Rural Local data is updated yearly, averaging 809,696.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 881,206.000 Mile in 2008 and a record low of 679,926.000 Mile in 1993. United States Public Road Length: Paved: Rural Local 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.

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.

This statistic represents the length of the largest road networks worldwide as of 2018, by country. The U.S. road network encompasses approximately **** million kilometers of paved and unpaved roads.

Forecast: Unpaved Roads Length in the US 2024 - 2028 Discover more data with ReportLinker!

Road edges are defined as the edge of the improved surface including the improved shoulder but do not include the unimproved shoulder, only the travel part of the road. The road network is compiled to include all open intersections. Features do not overlap sidewalks, but have the sidewalk area cut out of the road polygons. Overlapping features are acceptable if one of the features is hidden. Road: A generally named thoroughfare, that is usually paved and can be public or private. Unimproved thoroughfares are excluded. Road polygons are formed by a combination of road edge, curb, sidewalk, street intersection closure line, and map sheet edge. Paved Median Island: Perimeter of non-traffic paved areas that separate traffic lanes in opposing directions. Unpaved Median Island: Perimeter of non-traffic grassy, unpaved areas that separate traffic lanes in opposing directions. Paved Traffic Island: Perimeter of non-traffic concrete areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Unpaved Traffic Island: Perimeter of non-traffic unpaved, grassy areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Alley: Perimeter of alleys first plotted photogrammetrically from other indicators such as building footprints, fence lines, curb lines, walls, paved or unpaved drives, and map sheet edge. Alley polygons are closed along the lines where they intersect with road polygons. Paved Drive: A paved driveway for a building or entranceway for a parking lot. Driveways are neither streets nor alleys, but provide access to public facilities, such as a drive to a monument, museum, hotel, large estate, sports field or golf course, grounds of the U.S. Capitol, etc. If a driveway is less than 200 feet and leads to a parking lot, the entire paved area is captured as Parking Lot. Driveways are photogrammetrically compiled as polygons and not compiled from individual vectors on different levels. Parking Lot: Generally paved surfaces used for cars to park on. Paved drives usually form entrances to these features, if the drive is more than 200 feet. If the driveway is less than 200 feet leading into the parking lot, the entire paved area is captured as Parking Lot. Parking lots sharing a common boundary with linear features must have the common segment captured once, but coded as both polygon and line. Small parking areas, where individuals park their cars in the middle of a block off a public alley, are not captured as parking lots. These are either public space (e.g., alleys) or private space where owners permit parking to occur. Intersection: A location where more than one road comes together. For standard cross streets, intersection polygons are bounded by curbs and four closure lines at street intersection crosswalks (outer line) or placed arbitrarily where crosswalks could logically be placed. For "T" intersections, the polygons are bounded by curbs and three such closure lines. Complex intersections can have more closure lines. Entire traffic circles are coded as intersections. Hidden Road: A section of a road that passes underneath a bridge or overpass and is not visible in an aerial photograph, but the location can be interpreted based on the road on either side of the bridge. Hidden Median: A road median that exists underneath a bridge or overpass and is not fully visible in an aerial photograph, but the location can be interpreted based on the information visible on either side of the bridge.

This dataset provides values for ROADS PAVED PERCENT OF TOTAL ROADS WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States Public Road Length: Unpaved: Urban Local data was reported at 72,969.000 Mile in 2016. This records an increase from the previous number of 58,207.000 Mile for 2015. United States Public Road Length: Unpaved: Urban Local data is updated yearly, averaging 40,959.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 72,969.000 Mile in 2016 and a record low of 31,781.000 Mile in 1995. United States Public Road Length: Unpaved: Urban Local data remains active status in CEIC and is reported by Federal Highway Administration. The data is categorized under Global Database’s USA – Table US.TA001: Public Road and Street Length.

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 0.5791 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 0.023 and 0.1669 (in million kms), corressponding to 3.9642% and 28.8242% respectively of the total road length in the dataset region. 0.3892 million km or 67.2116% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0021 million km of information (corressponding to 0.5353% 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.

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 ...

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.

Connecticut Buffered Roads is 1:24,000-scale base map data. This layer is intended to be used with the Roads and Trails layer to reproduce the cartographic symbology established by the USGS for printing roads and trails on the 1:24,000-scale, 7.5-minute topographic quadrangle maps. Cartographically, the Buffered Roads layer is used to assign thin, black line symbology to the edges or curb lines of paved and unpaved roads on the quadrangle maps. Paved roads are symbolized with a narrow solid black line. Unpaved roads are symbolized with a narrow dashed black line. Complementing this symbology, the Roads and Trails layer is used to assign line symbology that 'fills in' the corresponding buffered road area with solid red or dashed red line work, depending on road class. Line symbology should be assigned to Roads and Trails features with AV_LEGEND attribute values equal to Primary Route (wide solid red), Secondary Route (wide dashed red), and Trail (narrow dashed black). Used in combination, Buffered Roads symbology outlines the centerline-based symbology applied to the Roads and Trails layer. For base map purposes, use this layer with other 1:24,000-scale base map data such as Hydrography, Railroads, Airports, and Towns. The Buffered Roads layer includes information within Connecticut and is derived from the Buffered Roads Master layer, which reproduces all buffered road features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut. This layer is a cartographic product and should only be printed on maps at 1:24,000 scale (1 inch = 2,000 feet.). Connecticut Buffered Roads is a 1:24,000-scale, feature-based layer of paved and unpaved roads on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. This layer is cartographic in nature. It is designed to be used with maps printed at 1:24,000-scale that require road symbology similar to the standard established by the USGS for 1:24,000-scale, 7.5 minute topographic quadrangle maps. Two layers, the Buffered Roads layer and the Roads and Trails layer, are used together for this purpose. Buffered Roads features are linear and run parallel to the road (centerline) features of the Roads and Trails layer. Buffered Roads is a set of parallel lines 50 feet apart that result from a buffer on each side of the Road and Trail (centerline) features by a distance of 25 feet. A width of 50 feet is applied to all roads, regardless of road class, and does not reflect actual pavement width. The Buffered Roads layer does not include features on the topographic quadrangle maps that appear as single lines such as hiking trails, small private roads, and old railroad grades. These features are found in the more complete Roads and Trails layer. The Buffered Roads layer is derived from information from USGS topographic quadrangle maps published between 1969 and 1984 and does not represent the road network in Connecticut at any one particular point in time. The layer does not depict current conditions and excludes many roads that have been built, modified, or removed since the time these topographic quadrangle maps were published. The layer includes buffered centerlines for Interstate highways, US routes, state routes, local roads, unpaved roads, traffic circles, bridges, cul-de-sacs, etc. Trails are not included. Features are linear and approximate road curb lines at 1:24,000 scale. Attribute information is comprised of codes to cartographically represent (symbolize) paved and unpaved roads on a map. This layer was originally published in 1994. The 2005 edition includes the same road features published in 1994, but the attribute information has been slightly modified and made easier to use.

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 1.2175 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 0.1437 and 0.0911 (in million kms), corressponding to 11.805% and 7.4802% respectively of the total road length in the dataset region. 0.9827 million km or 80.7148% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0113 million km of information (corressponding to 1.1482% 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.

Singapore was the country with the highest road quality in 2019. Singapore received a rating of *** on a scale of 1 (= under-developed) to 7 (= extensively developed according to international standards). Singapore was also ranked first in terms of efficient air transport services in 2019. The countries with the highest road quality Infrastructure is an important factor for the productivity, safety and satisfaction in a country. Roads are used daily for a variety of reasons, and in order to build and maintain roads, costs are often high for the government. A poor road quality could also lead to potential accidents and carelessness. Road injuries are among the 10 leading causes of death worldwide, taking ****** lives in 2019 in the U.S. alone. Not only does the government have to appropriately divide the territory, it is also important that these roads have a high efficiency to allow commuters to reach their preferred destinations with as few struggles as possible. This is particularly important in larger countries, where cities are further spread out from each other. In terms of mileage, the United States has the longest and largest road network in the world, with approximately *** million kilometers of paved or unpaved roads, while road density is highest in Western Europe and Asia.

United States Public Road Length: Paved: Urban Local data was reported at 783,113.000 Mile in 2016. This records a decrease from the previous number of 795,896.000 Mile for 2015. United States Public Road Length: Paved: Urban Local data is updated yearly, averaging 637,631.000 Mile from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 795,896.000 Mile in 2015 and a record low of 513,230.000 Mile in 1992. United States Public Road Length: Paved: Urban Local 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.

This Road Edge (Road Edge of Pavement) feature layer is comprised of features identifiable in the orthoimagery collected for Leon County, FL. Road edges were extracted from the impervious surfaces data from 2015 using paved roads, unpaved roads, paved driveways, and unpaved driveways. This dataset is part of a regularly scheduled update of LiDAR and digital orthophotography products. The dataset was created from source imagery acquired by a Trimble TAC80 natural color digital camera and LAS data acquired by a Optech ALTM HA500 (Pegasus) LIDAR sensor from January 18, 2015 to February 5, 2015.