TransportationThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, displays primary roads, secondary roads, local roads and railroads in the United States. According to the USCB, "This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways."Interstates 20 and 635Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (TIGERweb/Transportation) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 155 (Series Information for All Roads County-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Transportation - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Census Feature Class Codes (CFCC)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

GIS Maps, Transportation Data, and Reports for all modes of travel throughout Massachusetts.

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 ...

The City of Seattle Transportation GIS Datasets | https://data-seattlecitygis.opendata.arcgis.com/datasets?t=transportation | Lifecycle status: Production | Purpose: to enable open access to SDOT GIS data. This website includes over 60 transportation-related GIS datasets from categories such as parking, transit, pedestrian, bicycle, and roadway assets. | PDDL: https://opendatacommons.org/licenses/pddl/

| The City of Seattle makes no representation or warranty as to its accuracy. The City of Seattle has created this service for our GIS Open Data website. We do reserve the right to alter, suspend, re-host, or retire this service at any time and without notice.

| Datasets: 2007 Traffic Flow Counts, 2008 Traffic Flow Counts, 2009 Traffic Flow Counts, 2010 Traffic Flow Counts, 2011 Traffic Flow Counts, 2012 Traffic Flow Counts, 2013 Traffic Flow Counts, 2014 Traffic Flow Counts, 2015 Traffic Flow Counts, 2016 Traffic Flow Counts, 2017 Traffic Flow Counts, 2018 Traffic Flow Counts, Areaways, Bike Racks, Blockface, Bridges, Channelization File Geodatabase, Collisions, Crash Cushions, Curb Ramps, dotMaps Active Projects, Dynamic Message Signs, Existing Bike Facilities, Freight Network, Greater Downtown Alleys, Guardrails, High Impact Areas, Intersections, Marked Crosswalks, One-Way Streets, Paid Area Curbspaces, Pavement Moratoriums, Pay Stations, Peak Hour Parking Restrictions, Planned Bike Facilities, Public Garages or Parking Lots, Radar Speed Signs, Restricted Parking Zone (RPZ) Program, Retaining Walls, SDOT Capital Projects Input, Seattle On Street Paid Parking-Daytime Rates, Seattle On Street Paid Parking-Evening Rates, Seattle On Street Paid Parking-Morning Rates, Seattle Streets, SidewalkObservations, Sidewalks, Snow Ice Routes, Stairways, Street Design Concept Plans, Street Ends (Shoreline), Street Furnishings, Street Signs, Street Use Permits Use Addresses, Streetcar Lines, Streetcar Stations, Traffic Beacons, Traffic Cameras, Traffic Circles, Traffic Detectors, Traffic Lanes, Traffic Signals, Transit Classification, Trees.

GIS in Transportation Market Outlook

The global GIS in Transportation market size was valued at USD 9.5 billion in 2023 and is expected to reach USD 21.8 billion by 2032, growing at a CAGR of 9.5%. This rapid growth is driven by advancements in spatial data analytics and the increasing need for efficient transportation management systems across various sectors. The surge in urbanization, coupled with the rising adoption of smart city initiatives, has propelled the demand for geographic information systems (GIS) in transportation, making it an indispensable tool for urban planners and transportation authorities.

One of the primary growth factors in the GIS in Transportation market is the rising need for traffic management solutions. With increasing vehicle ownership and congested road networks, the implementation of GIS-based traffic management systems has become crucial. These systems help in real-time traffic monitoring, congestion management, and route optimization, thereby enhancing overall transportation efficiency. Additionally, the integration of GIS with Internet of Things (IoT) devices and sensors provides valuable data to city planners and traffic authorities, enabling better decision-making and improved traffic flow.

Another significant driver for the market is the growing emphasis on asset management in the transportation sector. GIS technology plays a pivotal role in tracking and managing transportation infrastructure assets such as roads, bridges, and tunnels. By leveraging GIS, transportation agencies can efficiently monitor the condition of these assets, schedule maintenance activities, and allocate resources effectively. This not only extends the lifespan of infrastructure assets but also ensures safety and reduces operational costs, thus driving the adoption of GIS in the transportation sector.

Moreover, the increasing focus on sustainable and eco-friendly transportation solutions is fostering the growth of the GIS in Transportation market. Governments and transportation authorities worldwide are promoting the use of public transit and non-motorized transportation modes to reduce carbon emissions and combat climate change. GIS technology aids in public transit planning and route optimization, ensuring efficient and sustainable transportation systems. Additionally, GIS-based solutions enable the assessment of environmental impacts and support the implementation of green transportation initiatives, further bolstering market growth.

Regionally, North America holds a significant share in the GIS in Transportation market, attributed to the early adoption of advanced technologies and substantial investments in transportation infrastructure. The presence of key market players and the implementation of smart city projects in the United States and Canada further drive the market's growth in this region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, propelled by rapid urbanization, increasing government initiatives for smart transportation, and the expansion of transportation networks in countries like China and India.

Component Analysis

The GIS in Transportation market is segmented by component into software, hardware, and services. The software segment dominates the market, driven by the rising demand for advanced GIS applications that provide comprehensive spatial analysis, mapping, and visualization capabilities. GIS software solutions, such as geographic information systems for traffic management and route planning, are extensively utilized by transportation authorities and urban planners to improve operational efficiency and decision-making processes. The continuous evolution of GIS software, incorporating advanced features like real-time data integration and predictive analytics, further propels market growth.

Hardware components, although smaller in market share compared to software, play a crucial role in the GIS in Transportation market. Hardware components include GPS devices, sensors, and data collection tools, which are essential for gathering accurate spatial data. The increasing deployment of IoT devices and sensors in transportation infrastructure enhances data collection capabilities, thus supporting the effective implementation of GIS solutions. The integration of GIS hardware with software solutions provides a holistic approach to transportation management, driving the adoption of GIS technology in this sector.

The services segment encompasses a wide range of professional services, including consulting, implementation, and maint

This dataset includes a multimodal assessment of the Cleveland Transportation Network, conducted as part of the Cleveland Moves initiative. It assesses need and comfort levels as we work to improve safety and mobility on Cleveland streets.The Pedestrian Crossing Level of Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, and the presence of a pedestrian island to calculate how stressful a crossing is for someone crossing. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The Bicycle Level of Traffic Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, the type of bikeway, and more to calculate the level of stress for someone riding a bicycle on a given street. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The ODOT Active Transportation Need layer was created by the Ohio Department of transportation, and uses several factors to determine need including access to a vehicle, poverty rates, and more.Update FrequencyThis dataset will be updated with additional analysis from the Cleveland Moves planning process by early 2025. After that point, it will be updated annually to reflect changes to Cleveland streets geared towards improving safety and mobility. Related ApplicationsA summary of this dataset can be found in the Cleveland Moves Network Assessment Dashboard.Data GlossaryThe ODOT Active Transportation Need dataset was developed by the Ohio Department of Transportation. More information about this dataset is available on their website: https://gis.dot.state.oh.us/tims_classic/Glossary ContactSarah Davis, Active Transportation Senior Plannersdavis2@clevelandohio.gov

The FDOT GIS Roads with Local Names feature class provides spatial information on local name of the roadway. The name given to a section of roadway to identify it from other sections of roadway. Local names are important for emergency medical services and law enforcement. This information is required for all roadways, including Active Exclusives. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/localnam.zip

Formerly published as TRANS_NOC_GTLF_PUB_UNK_LINE This standard houses linear features on BLM lands as well as transportation features that provide access to BLM transportation routes. In order to meet the local or state field office needs, each state may extend its GTLF data standard to collect data to fulfill local data requirements as long as the state or field office data can be cross-walked into the National GTLF data standard format. This dataset is an ongoing process, updated periodically when better data is available. The National BLM GTLF data provides standardized transportation information for use in BLM programs and analysis. At the core, this standard stores transportation information gathered, inventoried, analyzed and recorded in a BLM Transportation Management Plan or TMP. The TMP analyzes transportation options and incorporates public participation to assist in determining travel routes, modes of travel, and season of use on selected and surrounding BLM managed lands. The Idaho BLM maintains only GTLF routes that have been recorded in a TMP. This route data is identified by COORD_SRC2 = ‘TMP – Plan Name’ and PLAN_ROUTE_DSGNTN_AUTH = ‘BLM’. All other data is considered Non-BLM authoritative, administered and maintained transportation data and is provided only as spatial and tabular reference material. As new or existing transportation data is analyzed and recorded through the TMP process, the resulting data will be incorporated into the Idaho BLM GTLF authoritative data maintenance program. The Idaho BLM GTLF data contains transportation data from many different sources across the state including the TMP process, and as such may contain a variety of spatial and tabular inaccuracies and may include errors of omission or commission. The purpose for collecting and maintaining an Idaho state-wide compilation is to provide a broad overview of the transportation network for properly focused analysis or inventory purposes and to provide connectivity between BLM managed routes and the broader transportation network. As with all spatial data, this Idaho BLM GTLF data only represents an approximate or generalized spatial and tabular description of the actual transportation route. All BLM disclaimers apply to this data and that the use of this data is at the risk of the user. Additional Notes. The purpose of this dataset is to create an Idaho BLM feature class with existing routes. Due to the lack of finalized Travel Management Plans (TMP) throughout the state the best available data was used. Where TMP data was not available we used a larger collection of 1:24,000-scale "Resource Base Data" GIS road features gathered by Idaho BLM and a variety of other data collected from the state. May 4 2018: fields added for use by ID BLM for cartographic purposes. Jan 2020 update to GTLF Schema v3.0. This data set was created and is maintained by the Bureau of Land Management staff in Idaho for only those roads identified by the metadata. All other roads are included only as background source material and are not maintained in GIS or on the ground by BLM. For more information contact us at blm_id_stateoffice@blm.gov.

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

The FDOT Historical Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. It contains five years of AADT data including the most currently available year. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 06/21/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt_historical.zip

Downloadable GIS data about Virginia - focusing on infrastructure and transportation. Is downloadable and includes metadata.

VDOT's mission is to plan, deliver, operate and maintain a transportation system that is safe, enables easy movement of people and goods, enhances the economy and improves our quality of life.VDOT ArcGIS Online is an interactive portal through which VDOT staff, business partners, and the public can access web mapping applications, map publications, and geospatial data pertaining to transportation in Virginia. Users can learn about, browse, search, and/or download data from this site.The products on this site are for informational purposes and may not have been prepared for legal, engineering or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information.Questions? Contact the Spatial Intelligence Group.

This datasets contain GIS shapefiles related to the transportation infrastructure of Accomack and Northampton Counties on the Eastern Shore of Virginia. Included here are roads and highways, railroads, airfields and airstrips, boat ramps, and electrical transmission lines. Data was compiled from multiple sources. The primary purpose of this dataset is to provide VCRLTER researchers and students with a convenient up-to-date set of GIS data layers in one location that can be used as base layers for various map products and for planning research activities. A secondary purpose of this dataset is to extend transportation data coverage in the VCRLTER data catalog to include Accomack County and to supersede older USGS DLG data contained in the Northampton County GIS data package (VCRLTER dataset VCR14219).

This map layer is a subset of the Columbus Points of Interest layer and shows transportation facilities in the City of Columbus. Transportation facilities include airports, bus stops, parking lots, and parking garages. This layer is maintained through a cooperative effort by multiple departments of the City of Columbus using first-hand knowledge of the area as well as a variety of authoritative data sources. While significant effort is made to ensure the data is as accurate and comprehensive as possible, some points of interest may be excluded and included points may not be immediately updated as change occurs.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Road Centerline GIS Data exported from the CIty's GIS. See summary description (txt) file for information about intended use, projection, currency, attributes, etc.

This map data layer represents the centerline of roadways for the City of Bloomington, Indiana. It includes source data from the City of Bloomington and Monroe County to create a countywide network. It includes public roads, named private roads, major multi-use trails, and proposed roadways to be constructed. Centerlines outside the City's mapped area may not be positioned accurately.

Roadway edge of pavement features exported from the CIty's GIS. See summary description (txt) file for information about intended use, projection, currency, attributes, etc.

This map data layer represents the edge of pavement of roadways for the City of Bloomington, Indiana. It was created and updated from aerial photography. Continual updates are made as needed. It includes paved roads, gravel roads, and bridges. It includes some improved alleys, access drives, travel lanes, and other miscellaneous road features. Features may be identified as coincident to building and parking structures.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

This feature class is the BLM Natl GTLF Public Motorized Roads subset of the feature dataset containing the BLM Ground Transportation Linear Features. A linear feature for ground transportation includes roads, primitive roads, primitive routes, trails, temporary routes, and linear disturbances. The Ground Transportation Linear Feature (GTLF) data standard provides a national geospatial data standard of the ground transportation linear features in BLM’s Enterprise GIS (E-GIS). A national BLM GTLF data standard is essential for collecting the landscape-scale data necessary to identify management opportunities and challenges that may not be evident when managing smaller land areas. GTLF data not only serve the crucial function of improving BLM transportation planning, but is also invaluable to numerous other BLM programs affected by transportation (e.g. water and air quality, wildlife habitat fragmentation, engineering, realty, cultural resources). This dataset is a subset of the official national dataset, containing features and attributes intended for public release and has been optimized for online map service performance. The Schema Workbook represents the official national dataset from which this dataset was derived.

The National Transit Map - Stops dataset was compiled on June 02, 2025 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). The National Transit Map (NTM) is a nationwide catalog of fixed-guideway and fixed-route transit service in America. It is compiled using General Transit Feed Specification (GTFS) Schedule data. The NTM Stops dataset shows stops where vehicles pick up or drop off riders. This dataset uses the GTFS stops.txt file. The GTFS schedule format and structure documentation is available at, https://gtfs.org/schedule/. To improve the spatial accuracy of the NTM Stops, the Bureau of Transportation Statistics (BTS) adjusts transit stops using context from the submitted GTFS source data and/or from other publicly available information about the transit service. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529049