Note - this data is intended to be used with the "Maryland Road-Separated Bicycle Routes" hosted feature (view) layer hosted by MDOT and not in isolation.Maryland Road-Separated Connectors data comprises linear geometric features which represent the connections for bicycle routes that are separated from roadways carrying motorized vehicle traffic throughout the State of Maryland to road centerlines. This data is primarily used for the purposes of network analysis and in many instances, the 'connectors' are GIS vector creations and not true, paved, bicycle connections. This data is complimentary and to be used in conjunction with the "Maryland Road-Separated Bicycle Routes" hosted feature view layer (also hosted by MDOT). That data - and these connections from roadway to that data - are used to map Bicycle routes that are Shared-Use Paths, typically 10-feet wide, which can be used for transportation or recreational-related purposes.ATTRIBUTES:Route Name (if Applicable): The name of the route is provided if the route is namedCounties within Route: The counties in Maryland through which the route passes are listedRoute's Length: The route distance is calculated and listed in miles. Note that this is the length of the entire named route - and not just the segment selected. Distance calculated using the NAD 1983 StatePlane Maryland FIPS 1900 (US Feet) Projection.LTS Score: Level of Traffic Stress. For this map (road-separated routes) the scores range from 0 (road-separated) to 2 (generally low traffic). The areas that are not 0 in this map/data represent portions of the road-separated routes that cross streets or have portions that are briefly on-road as connections.

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.

This dataset holds a vector layer for the road network in Lamto research station in 1988. To produce the dataset we digitized the roads from the map “Carte du recouvrement ligneux de la réserve de Lamto" published by Gautier in 1990. Some of the roads no longer exist in 2021. The attributes of the shapefile follow the OpenStreetMap (OMS) data schema. Roads classified as "piste principale" on the original map have the OMS attributes "unclassified" or "residential". Roads classified as "piste secondaire" on the original map have the OMS attribute "track". The type "sentiers" is classified as "footway".

A vector line file of public/private streets compiled from orthoimagery and other sources that is attributed with street names, addresses, route numbers, routing attributes, and includes a related table of alternate/alias street names.

Road centerlines are vector lines that represent the geographic center of road-right-of-ways on transportation networks and are utilized in many different applications, from address geocoding to vehicle routing.

This dataset holds a vector layer for the road network in Lamto research station in 1963. To produce the dataset we digitized the roads from the unpublished map “Carte physionomique des faciès savanians de Lamto" drawn by de la Souchère; P. and Badarello, I. in 1969 and the mosaic of aerial photographs acquired by IGN in 1963. Most of the footpaths no longer exist in 2021. The attributes of the shapefile follow the OpenStreetMap (OMS) data schema.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

Data file name: Beijing.rar Data deion: 1) after finishing public issued of Beijing city traffic figure, and Beijing map, and Beijing Tourism figure, by geometry corrected, and image distribution associate, work Hou, on the year road center line for vector quantitative, on vector quantitative of network data for edit, until network full, get has Beijing city five ring within, each 10 years around time interval of network GIS data, established has Beijing history network data set. 2) data file contains years of Beijing's road network data and route data is shapefile files and named for years (1969, 1978, 1990, 2000 and 2008). 3) shapefile file's property sheet for each year, the field "year_" section belongs to the year, the field "From_" indicates that this stretch of road network from previous vintages in the sections corresponding to the FID.

If you have any questions, please contact lianggao@bjtu.edu.CN.

This is the transportation data from the Ordnance Survey Meridian 2 product. It contains Motorways, A roads, B roads, minor roads, roundabouts, junction numbers, railways, railway stations and the coastline. Each feature is in a separate Shapefile. Each Shapefile covers the whole of Great Britain. From the Ordnance Survey Technical Information 'The road network in Meridian 2 is a derived and simplified network which has been produced from the Roads database. The simplified data provides a good entry level road network with the advantage of low data volumes to reduce processing time.'. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-20 and migrated to Edinburgh DataShare on 2017-02-22.

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

This polyline shapefile depicts centerlines for roads located in the County of Solano, California. Road centerlines are vector line data that represent the geographic center of road rights-of-way on transportation networks. This dataset includes the full name of each road, as well as the cities and postal codes in which roads are located. This layer is part of a collection of GIS data produced by Solano County, California.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset contains 4 different scale GEODATA TOPO series, Geoscience Australia topographic datasets. 1M, 2.5M, 5M and 10M with age ranges from 2001 to 2004.

1:1 Million - Global Map Australia 1M 2001 is a digital dataset covering the Australian landmass and island territories, at a 1:1 million scale. Product Specifications -Themes: It consists of eight layers of information: Vector layers - administrative boundaries, drainage, transportation and population centres Raster layers - elevation, vegetation, land use and land cover -Coverage: Australia -Currency: Variable, based on GEODATA TOPO 250K Series 1 -Coordinates: Geographical -Datum: GDA94, AHD -Medium: Free online -Format: -Vector: ArcInfo Export, ESRI Shapefile, MapInfo mid/mif and Vector Product Format (VPF) -Raster: Band Interleaved by Line (BIL)

1:2.5 Million - GEODATA TOPO 2.5M 2003 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 2.5 million general reference map and is suitable for GIS applications. The product consists of the following layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; Spot heights; and waterbodies. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 1:2.5 million scale general reference maps. This data supersedes the TOPO 2.5M 1998 product through the following characteristics: developed according to GEODATA specifications derived from GEODATA TOPO 250K Series 2 data where available. Product Specifications Themes: GEODATA TOPO 2.5M 2003 consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; spot heights; and waterbodies Coverage: Australia Currency: 2003 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online - Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif

1:5 Million - GEODATA TOPO 5M 2004 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 5 million general reference map and is suitable for GIS applications. Offshore and sand ridge layers were sourced from scanning of the original 1:5 million map production material. The remaining nine layers were derived from the GEODATA TOPO 2.5M 2003 dataset. Free online. Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif. Product Specifications: Themes: consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges, spot heights and waterbodies Coverage: Australia Currency: 2004 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online

1:10 Million - The GEODATA TOPO 10M 2002 version of this product has been completely revised, including the source information. The data is derived primarily from GEODATA TOPO 250K Series 1 data. In October 2003, the data was released in double precision coordinates. It provides a fundamental base layer of geographic information on which you can build a wide range of applications and is particularly suited to State-wide and national applications. The data consists of ten layers: built-up areas, contours, drainage, Spot heights, framework, localities, offshore, rail transport, road transport, and waterbodies. Coverage: Australia Currency: 2002 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, Arcview Shapefile and MapInfo mid/mif Medium: Free online

Dataset History

1:1Million - Vector data was produced by generalising Geoscience Australia's GEODATA TOPO 250K Series 1 data and updated using Series 2 data where available in January 2001. Raster data was sourced from USGS and updated using GEODATA 9 Second DEM Series 2, 1:5 million, Vegetation - Present (1988) and National Land and Water Resources data. However, updates have not been subjected to thorough vetting. A more detailed land use classification for Australia is available at www.nlwra.gov.au.

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_48006

1:2.5Million - Data for the Contours, Offshore, and Sand ridge layers was captured from 1:2.5 million scale mapping by scanning stable base photographic film positives of the original map production material. The key source material for Built-up areas, Drainage, Spot heights, Framework, Localities, Rail transport, Road transport and Waterbodies layers was GEODATA TOPO 2.5M 2003

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60804

1:5Million - Offshore and Sand Ridge layers have been derived from 1:5M scale mapping by scanning stable base photographic film positives of the various layers of the original map production material. The remaining layers were sourced from the GEODATA TOPO 2.5M 2003 product.

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_61114

1:10Million - The key source for production of the Builtup Areas, Drainage, Framework, Localities, Rail Transport, Road Transport and Waterbodies layers was the GEODATA TOPO 250K Series 1 product. Some revision of the Builtup Areas, Road Transport, Rail Transport and Waterbodies layers was carried out using the latest available satelite imagery. The primary source for the Spot Heights, Contours and Offshore layers was the GEODATA TOPO 10M Version 1 product. A further element to the production of GEODATA TOPO 10M 2002 has been the datum shift from the Australian Geodetic Datum 1966 (AGD66) to the Geocentric Datum of Australia 1994 (GDA94).

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60803

Dataset Citation

Geoscience Australia (2001) Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/310c5d07-5a56-4cf7-a5c8-63bdb001cd1a.

Esri ArcGIS Online (AGOL) Hosted Feature Layer which provides access to the MDOT SHA Roadway Functional Classification data product.MDOT SHA Roadway Functional Classification data consists of linear geometric features which showcase the functional classification of roadways throughout the State of Maryland. Roadway Functional Classification is defined as the role each roadway plays in moving vehicles throughout a network of highways. MDOT SHA Roadway Functional Classification data is primarily used for general planning purposes, and for Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) annual submission & coordination. The Maryland Department of Transportation State Highway Administration (MDOT SHA) currently reports this data only on the inventory direction (generally North or East) side of the roadway. MDOT SHA Roadway Functional Classification data is not a complete representation of all roadway geometry.The State of Maryland's roadway system is a vast network that connects places and people within and across county borders. Planners and engineers have developed elements of this network with particular travel objectives in mind. These objectives range from serving long-distance passenger and freight needs to serving neighborhood travel from residential developments to nearby shopping centers. The functional classification of roadways defines the role each element of the roadway network plays in serving these travel needs. ​ Over the years, functional classification has come to assume additional significance beyond its purpose as a framework for identifying the particular role of a roadway in moving vehicles through a network of highways. Functional classification carries with it expectations about roadway design, including its speed, capacity and relationship to existing and future land use development. Federal legislation continues to use functional classification in determining eligibility for funding under the Federal-aid program. Transportation agencies describe roadway system performance, benchmarks and targets by functional classification. As agencies continue to move towards a more performance-based management approach, functional classification will be an increasingly important consideration in setting expectations and measuring outcomes for preservation, mobility and safety.MDOT SHA Roadway Functional Classification data is developed as part of the Highway Performance Monitoring System (HPMS) which maintains and reports transportation related information to the Federal Highway Administration (FHWA) on an annual basis. HPMS is maintained by the Maryland Department of Transportation State Highway Administration (MDOT SHA), under the Office of Planning & Preliminary Engineering (OPPE) Data Services Division (DSD). This data is used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Roadway Functional Classification data is key to understanding the role each roadway plays in moving vehicles throughout the State of Maryland's network of highways.MDOT SHA Roadway Functional Classification data is owned & maintained by the MDOT SHA Office of Planning & Preliminary Engineering (OPPE). This data product is updated & published on an annual basis for the prior year. This data product is for the year 2023.For more information related to the data, contact MDOT SHA OPPE Data Services Division (DSD):Email: DSD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This is a data output from the GeoCrimeData project (http://geocrimedata.blogspot.com/). It contains Open Street Map data with derived measures of road integration (which can be used as a proxy for traffic volume). The data were derived from Open Street Map downloaded provided on the ShareGeo repository (e.g. for England: http://www.sharegeo.ac.uk/handle/10672/28) For more information about how the data was created, see: https://docs.google.com/document/d/16eNQKKxlLlh8H2Gayz86F68ZsTXUF72kJ1qiW2VUu7A/edit For other GeoCrimeData written material, see: https://docs.google.com/document/d/1gJ9B4BZNvL3w2DPfyv9vu-7P7_tnVf3F3H3rvygr1cc. Map data (c) OpenStreetMap contributors, CC-BY-SA This dataset was derived from OpenStreetMap. Access and use constraints are based on conditions set out in the OpenStreetMap Licence Agreement which can be found at http://wiki.openstreetmap.org/wiki/OpenStreetMap_License. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-11-10 and migrated to Edinburgh DataShare on 2017-02-21.

The vector data is updated utilizing positions calculated from plats using coordinate geometry programs. Plated, Public road centerlines are captured within this database. Private roads may not be shown.

The centerlines usually represent the center of the physical roadway pavement. The center of physical roadway pavement may or may not represent the center of the road right of way. Road right of ways may taper or change width.

This file has been further modified using several sources including survey field data and digitizing off aerial photos. Attributes have been included to allow geo-coding and the support of Washington County Sheriff's Office Communication Center. Regional Data is available through the MN Geocommons. https://gisdata.mn.gov/dataset/us-mn-state-metrogis-trans-road-centerlines-gac

The Road Network File is a digital representation of Canada's national road network, containing information such as street names, types, directions and address ranges. User applications of this file may include mapping, geocoding, geographic searching, area delineation, and database maintenance as a source for street names and locations. Since statistical activities do not require absolute positional accuracy, relative positional accuracy takes precedence in the Road Network File. As a result, this file is not suitable for engineering applications, emergency dispatching services, surveying or legal applications. A reference guide is included. Data sourced from http://www.statcan.gc.ca/, please reference this as the source if you re-use the data. More details about the roads dataset can be found here: http://www12.statcan.gc.ca/census-recensement/2011/geo/RNF-FRR/index-eng.cfm. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-02-13 and migrated to Edinburgh DataShare on 2017-02-21.

Please read: This is the table for Road Section Geometry, which is part of the set of NZ Roads tables. The Road Section Geometry table stores the linear geometry for the associated road section, or part of the associated road section.

The NZ Roads dataset includes eight data tables and eleven lookup tables. The dataset has been sourced from LINZ’s NZ Roads database, a database for the management of national roads, including those managed for addressing purposes. This set of normalised tables replaces the Landonline: Road Centre Line layer and the Landonline: Road Name and Landonline: Road Name Association tables currently published on LDS.

These centrelines are required to indicate the presence of an authoritative road name. Named centrelines are not intended to represent the exact location of a road formation. Named centrelines do not indicate the presence of legal access.

For a simplified version of the data contained within these tables see NZ Roads (Addressing), which aggregates geometries based on road name, and NZ Roads Subsections (Addressing), which holds the individual geometries.

Please refer to the NZ Roads Data Dictionary for detailed metadata and information about this layer.

The Highway Performance Monitoring System (HPMS) 2020 dataset was compiled February 28, 2022 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 file geodatabase download provides HPMS data for each state as an individual feature class. This dataset represents the highway system as of the 2020 calendar year. HPMS provides data that reflects the extent, use, condition, and performance of the public roads in the United States. It consists of the All Road Network of Linear Referenced Data (ARNOLD) geometry and the Section Data which is the attribution. ARNOLD and Sections are linked though linear referencing and are part of the HPMS data program. These data are analytical for the purpose of supporting transportation programs, funding and policy decisions at a national level. Operational applications such as navigation and routing may take advantage of HPMS with the understanding that it represents the average and not “real-time” of the system. The Highway Performance Monitoring System Field Manual contains a detailed description of each data element including coding instructions and attribute definitions. The Field Manual is available at: https://doi.org/10.21949/1519108.

The Right of Way Map Footprint is a GIS data set created to represent the outer footprint or extent of a right of way map (including footprints for both Vector (CAD) and Raster Images). The purpose is to aid the user in more rapidly identifying the desired map for a specific area of interest relative to other maps, roads, landmarks, etc. This data set is developed and maintained on a statewide basis. It does not include geo-referenced representations of right of way maps themselves.

Maryland Bicycle Level of Traffic Stress (LTS) An overview of the methodology and attribute data is provided below. For a detailed full report of the methodology, please view the PDF published by the Maryland Department of Transportation here. The Maryland Department of Transportation is transitioning from using the Bicycle Level of Comfort (BLOC) to using the Level of Traffic Stress (LTS) for measuring the “bikeability” of the roadway network. This transition is in coordination with the implementation of MDOT SHA’s Context Driven Design Guidelines and other national and departmental initiatives. LTS is preferred over BLOC as LTS requires fewer variables to calculate including:Presence and type of bicycle facilitySpeed limitNumber of Through Lanes/Traffic VolumeTraditionally, the Level of Traffic Stress (LTS) (scale “1” to “4”) is a measure for assessing the quality of the roadway network for its comfort with various bicycle users. The lower the LTS score, the more inviting the bicycle facility is for more audiences.LTS Methodology (Overview) MDOT’s LTS methodology is based on the metrics established by the Mineta Transportation Institute (MTI) Report 11-19 “Low-Stress Bicycling and Network Connectivity (May 2012) - additional criteria refined by Dr. Peter G. Furth (June 2017) below and Montgomery County's Revised Level of Traffic Stress. Shared-use Path Data Development and Complimentary Road Separated Bike Routes DatasetA complimentary dataset – Road Separated Bike Routes, was completed prior to this roadway dataset. It has been provided to the public via (https://maryland.maps.arcgis.com/home/item.html?id=1e12f2996e76447aba89099f41b14359). This first dataset is an inventory of all shared-use paths open to public, two-way bicycle access which contribute to the bicycle transportation network. Shared-use paths and sidepaths were assigned an LTS score of “0” to indicate minimal interaction with motor vehicle traffic. Many paved loop trails entirely within parks, which had no connection to the adjacent roadway network, were not included but may be included in future iterations. Sidepaths, where a shared-use path runs parallel to an adjacent roadway, are included in this complimentary Road Separated Bike Routes Dataset. Sidepaths do not have as an inviting biking environment as shared-use paths with an independent alignment due to the proximity of motor vehicle traffic in addition to greater likelihood of intersections with more roadways and driveways. Future iterations of the LTS will assign an LTS score of “1” to sidepaths. On-street Bicycle Facility Data Development This second dataset includes all on-road bicycle facilities which have a designated roadway space for bicycle travel including bike lanes and protected bike lanes. Marked shared lanes in which bicycle and motor vehicle traffic share travel lanes were not included. Shared lanes, whether sharrows, bike boulevards or signed routes were inventoried but treated as mixed traffic for LTS analysis. The bicycle facilities included in the analysis include:

Standard Bike Lanes – A roadway lane designated for bicycle travel at least 5-feet-wide. Bike lanes may be located against the curb or between a parking lane and a motor vehicle travel lane. Buffered bike lanes without vertical separation from motor vehicle traffic are included in this category. Following AASHTO and MDOT SHA design standards, bike lanes are assumed to be at least 5-feet-wide even through some existing bike lanes are less than 5-feet-wide.
Protected Bike Lanes – lanes located within the street but are separated from motor vehicle travel lanes by a vertical buffer, whether by a row of parked cars, flex posts or concrete planters. Shoulders – Roadway shoulders are commonly used by bicycle traffic. As such, roadways with shoulders open to bicycle traffic were identified and rated for LTS in relation to adjacent traffic speeds and volumes as well as the shoulder width. Shoulders less than 5-feet-wide, the standard bike lane width, were excluded from analysis and these roadway segments were treated as mixed traffic.

The Office of Highway Development at MDOT SHA provided the on-street bicycle facility inventory data for state roadways. The shared-use path inventory and on-street bicycle facility inventory was compiled from local jurisdiction’s open-source download or shared form the GIS/IT departments. Before integrating into OMOC, these datasets were verified by conducting desktop surveys and site visits, and by consulting with local officials and residents.
Data UsesThe 2022 LTS data produced through this process can be used in a variety of planning exercises. The consistent metrics applied across the state will help inform bicycle mobility and accessibility decisions at state and local levels. Primarily, the LTS analysis illustrates how bikeable Maryland roads are where the greatest barriers lie. While most roads in the state are an LTS 1, the main roadways which link residential areas with community services are typically LTS 4. In the coming months, MDOT will use the LTS in variety of way including:

Conducting a bicycle network analysis to develop accessibility measures and potential performance metrics. Cross-referencing with state crash location data; Performing gap analysis to help inform project prioritization.

Data Limitations A principle of data governance MDOT strives to provide the best possible data products. While the initial LTS analysis of Maryland’s bicycle network has many uses, it should be used with a clear understanding of the current limitations the data presents.

Assumptions - As noted earlier in this document, some of the metrics used to determine LTS score were estimated. Speed limits for many local roadways were not included in the original data and were assigned based on the functional classification of the roadway. Speed limits are also based on the posted speed limit, not the prevailing operating vehicle speeds which can vary greatly. Such discrepancies between actual and assumed conditions could introduce margins of error in some cases. As data quality improves with future iterations, the LTS scoring accuracy will also improve. Generalizations - MDOT’s LTS methodology follows industry standards but needs to account for varying roadway conditions and data reliability from various sources. The LTS methodology aims to accurately capture Maryland’s bicycle conditions and infrastructure but must consider data maintenance requirements. To limit data maintenance generalizations were made in the methodology so that a score could be assigned. Specifically, factors such as intersections, intersection approaches and bike lane blockages are not included in this initial analysis. LTS scores may be adjusted in the future based on MDOT review, updated industry standards, and additional LTS metrics being included in OMOC such as parking and buffer widths.
Timestamped - As the LTS score is derived from a dynamic linear referencing system (LRS), any LTS analysis performed reflects the data available in OMOC. Each analysis must be considered ‘timestamped’ and becoming less reliable with age. As variables within OMOC change, whether through documented roadway construction, bikeway improvements or a speed limit reduction, LTS scores will also change. Fortunately, as this data is updated in the linear referencing system, the data becomes more reliable and LTS scores become more accurate. --------------------------------------------------------------------------------------------------------------------------------------------------------------------Level of Traffic Stress (LTS) Attribute Metadata OBJECTID | GIS Object IDState ID (ID) | Unique identification number provided by Maryland State Highway Administration (MDOT SHA)Route ID (ROUTEID) | Unique identification number for the roadway segment/record as determined by Maryland State Highway Administration (MDOT SHA) From Measure (FROMMEASURE) | The mileage along the roadway record that the specific roadway conditions change and maintain the same conditions until To MeasureTo Measure (TOMEASURE) | The mileage along the roadway record that the specific roadway conditions change and maintain the same conditions since From MeasureRoadway Functional Class (FUNCTIONAL_CLASS) | The functional classification of the roadway as determined by the Federal Highway Administration in coordination with the Maryland Department of Transportation State Highway Administration (MDOT SHA). All roadway records have a functional classification value. The following values represent the functional classification:

1 - Local 2 - Minor collector 3 - Major collector 4 - Minor arterial 5 - Principal Arterial (other) 6 - Principal Arterial (other Freeways and Expressways) 7 - Interstate

Annual Average Daily Traffic (AADT) | The Annual Average Daily Traffic (AADT) represents the average number of motor vehicles that pass along a roadway segment during a 24-hour period. The value is derived from MDOT SHA’s Traffic Monitoring System (TMS), the state’s clearinghouse for all traffic volume records. Roadway Speed Limit (SPEED_LIMIT) | The posted speed limit for a roadway segment as assigned by the MDOT SHA for state roadways and the local jurisdiction’s transportation management agency. Values for SPEED_LIMIT are measured in miles per hour (mph) in 5 mph increments from 5 mph through 70 mph. Roadway Access Control (ACCESS_CONTROL) | The access control indicates the types of entry points along the roadway segment, ranging from full to no access control. Interstates and other state roadways with no at-grade crossings are full access control, whereas a neighborhood street open to all modes of traffic represents a roadway with no access control. The values in