Transport data

Primary roads, secondary roads, local roads, and railroads; January 1, 2020

Ce jeu de données correspond à l'offre mobilité du réseau interurbain Rémi en Centre-Val de Loire. Les données proviennent de JVMALIN, la plateforme multimodale sur les transports en région. La plateforme est alimentée par les différents délégataires du réseau Rémi qui mettent à jour régulièrement les arrêts, les lignes, les itinéraires et les horaires qu'ils désservent. Les données sont actualisées sur ce jeu de données de manière hebdomadaire.

Census Current (2020) Physical Features Web Map Service; January 1, 2020 vintage

Primary roads, secondary roads, local roads, and railroads; January 1, 2020

Potential mobility projects for Strategic Investment Areas have been grouped together in an effort to provide adaptable and scale project investments. Each group of potential projects has a specific mobility focus for the set of potential projects it represents.

Blockface Segments are unbroken sections of the curb. A blockface can be made up of several subblocks, or just one subblock. There are blockfaces on both sides of the road, each dependent on an unbroken length of curb. For more information please visit DDOT's wiki page

The review of these roads was conducted by the Mobility Safety team as part of the Safer Mobility Plan. This dataset is used to track locations that have been reviewed and generate work plans for reducing road speed limits as well as communicating reviewed roads to the general public. This dataset is a public view layer created from CALGIS_MOBILITY_SAFETY_COLLECTOR_SPEED_LIMIT_REVIEWED_ROADS and is updated as needed via HYDRA. Data Steward: Mobility - Safety Email: MobilitySafety@calgary.ca

This dataset is used to map the location of Pharmacies in Mecklneburg County. Pharmacies are often inside of other businesses such as grocery stores or other retailers. Some pharmacies are standalone businesses.

Overview The Crash Information (Last 5 Years) dataset provides high-level information about reported crashes recorded in Western Australia during the previous five calendar years. Information includes crash location, date, time, severity and crash characteristics, together with information about the different road user and vehicle types involved. Crash data is recorded in the Integrated Road Information System (IRIS) and is provided for information purposes only. Refresh frequency The dataset is refreshed weekly. Intended use This dataset supports analysis of crash locations, trends, characteristics and outcomes. Refer to the data dictionary when interpreting fields, coded values and summary measures. Interpretation A crash may involve more than one unit type. Unit-type measures must not be added together to calculate the total number of crashes. Heavy trucks are also included within the truck category. Refer to the data dictionary for the definitions of each measure. Documentation The Main Roads Crash Data Dictionary describes the available attributes and provides guidance for understanding, interpreting and analysing the crash information, intersection summary and midblock summary datasets. Main Roads Crash Data Dictionary (PDF) https://mainroads.sharepoint.com/:b:/s/mr-opendata/IQDm4WzPtcQvS5q435Mc-LMlAXTqiR8kbkG3hNlSGl6Rc2Y?e=5UpZaG Related datasets Crash Summary for Midblocks (Last 5 Years) Crash Summary for Intersections (Last 5 Years) Data Governance Roles Data Domain Steward Road Safety Research and Evaluation Manager Data Custodian Asset and Geospatial Information Branch (agi@mainroads.wa.gov.au) Operational Data Steward Information Systems Administrator Licence Notice You are accessing this data pursuant to a Creative Commons Attribution Licence, which includes a disclaimer of warranties and limitation of liability. The data provided under the licence is subject to change. Pursuant to section 3 of the licence, the following notice is to be included when sharing the licensed material: The Commissioner of Main Roads is the creator and owner of the data and licensed material, which is accessed pursuant to a Creative Commons Attribution Licence, which has a disclaimer of warranties and limitation of liability. Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Mapping depicts Brampton Plan Schedule 3B Support Corridors for potential rapid transit.  Corridors are strategic linear growth areas located along major transportation routes where mixed-use development, intensification, employment, and enhanced mobility are encouraged. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.

Monaghan County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Monaghan County Council accepts no liability for any loss or damage suffered by those using this data for any purpose.  The Projection of this dataset is Irish Transverse Mercator.

Point locations of accessible parking bays in Galway City, published by Galway City Council. Each feature represents one accessible parking bay.

Point features marking car parking locations, published by Galway City Council. Each point represents a car parking site.

Line features representing bus routes, published by Galway City Council. Each feature records a route segment with a classification such as Secondary Route and its length.

Line features representing the N6 Galway City Ring Road, published by Galway City Council. Each feature records a road alignment with its length.

Primary roads, secondary roads, local roads, and railroads; January 1, 2020

Rehabilitation projects for roadways, sidewalks, alleys and markings from ProTrack+. When approved this point can be selected in project solicitation phase to associate it with a Solicitation Number and/or Contract Number. All project locations must be added during initiation. As the District Department of Transportation (DDOT) works to meet the goals of quality roadways, it has taken into account road quality assessment and community requests allowing the department to first address the streets that are in greatest need and highest priority to the community. Keeping DC roads in good condition provides maximum benefit to the traveling public by enhancing the safety of the roadway and improving the ride comfort of the road surface. View our weekly construction update to find out where DDOT crews are scheduled to be working over the coming days

Corridor Protection Areas identify lands reserved or protected for future transportation, infrastructure, utility, or transit corridors. These areas help preserve opportunities for long-term infrastructure improvements and ensure that future growth can be supported by strategic transportation and servicing investments. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.

Census Current (2020) Physical Features Web Map Service; January 1, 2020 vintage

Roads included in the US Forest Service's Motor Vehicle Use Map to a modified Kentucky boundary that includes the Tennessee areas of the Land Between the Lakes and Big South Fork National Recreation Area. The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the Motor Vehicle Use Map (MVUM). Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Information for each individual unit must be verified as to be consistent with the published MVUMs prior to inclusion in this data. Not every National Forest has data included in this feature class. Data Download: https://ky.box.com/v/kymartian-usfs-roads-muvm

A single line representation of the snow and ice clearance routes provided by HRM Transportation and Public Works. The route also indicate what material is used for street treatment such as salt or sand, and priority. The purpose of this dataset is to aid in the management of winter maintenance by tracking the priorities and treatment applied to routes. Maps are generated from this layer and provided to HRM staff and external contractors.  Metadata

YleiskuvausTämä aineisto sisältää Digiroadista poimittuja pääkatujen nimeämistietoja. Kohteina ovat katujaksoihin liittyvät viivamaiset tie- ja katuosuudet, joille on tallennettu nimimuotoja suomeksi ja ruotsiksi sekä tieto aineiston latausajankohdasta. SisältöKatusimien tiedot suomalaisina ja ruotsalaisina nimimuotoina. Päivitetty lähdeaineisto, jossa latausajankohta on tallennettu kohteille. Viivamaiset kohteet, jotka kuvaavat katuosuuksien sijaintia ja pituutta. KäyttötarkoitusKatujen nimien tarkastelu ja hakeminen kartalla. Paikannukseen ja osoitetietojen tukemiseen liittyvät GIS-sovellukset. Kaupunkialueiden katuverkon nimeämistiedon hyödyntäminen suunnittelussa ja kartoituksessa. Aineistolähde ja käyttöoikeusLähde on Digiroad, Väyläviraston ylläpitämä tie- ja katuverkon aineisto. Aineistoa saa käyttää CC BY 4.0 -lisenssin mukaisesti.

The data was created for mapping purposes from written descriptions of the bus routes, supplied by Halifax Transit. Metadata

Intersection Treatments include:Bike Boxes Bike Chevrons Bike Crossings Protected Intersections Roundabouts Traffic Circles Two Stage Bike Turns

The data is gathered to measure Halifax Transit's ridership. Some data errors have identified involving counters giving incorrect boardings, when these errors have been detected, the values have been removed from the data to avoid misleading data. Metadata The previous years data is available in the Transit Automated Passenger Counts Archived dataset.

Identifies areas of special character as set out in the policy framework of D18 Special Character Areas Overlay – Residential and Business and Schedule 14 of the Plan.

These resources offer non-emergency transportation to medical appointments, grocery stores, social activities, etc., and may be government or private services. This list includes programs supported through District funding for older adults, as well as services that offer support inclusive to individuals with dementia. Some of these services are available to all older adults, while others are restricted to members of organizations, such as the DC Villages. This resource guide does not include private rideshare companies such as Lyft, Uber, or Go Go Grandparent.

Line features representing greenways, each labelled as a greenway and recording its length. Published by Galway City Council and covering Ireland.

Core property information for properties within Upper Hutt City. Data is sourced from LINZ except for valuation number which is sourced from UHCC.

This dataset contains the daily count of transits on select maritime passageways during the following events of disruption: i) Suez Canal blockage, ii) Russian Invasion of Ukraine for Bosporus Strait, and iii) the Houthi attacks for Bab el-Mandeb Strait. The counts are derived from Automatic Identification System (AIS) data--high frequency ship signals during navigation--using the framework developed in the publication Analyzing anomalous events in passageways with high-frequency ship signals. Data to validate the transits and other supplementary dataof the publication are also included.

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

OpenStreetMap contains roughly 1.6 million km of roads in this region. Based on AI-mapped estimates, this is approximately 94 % of the total road length in the dataset region. The average age of data for the region is 1 year, 5 months ( Last edited 2 months ago ) and 11% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['highway'] IS NOT NULL Features may have these attributes: - name - name:en - highway - surface - smoothness - width - lanes - oneway - bridge - layer - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - name:en - amenity - building - port - operator:type - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['shop'] IN ('alcohol','art','bakery','beauty','beverages','bicycle','books','butcher','car','car_parts','car_repair','charcoal','chemist','clothes','convenience','copyshop','cosmetics','electronics','food','furniture','greengrocer','hairdresser','houseware','jewelry','kiosk','mobile_phone','pastry','shoes','stationary','supermarket','tailor','yes') Features may have these attributes: - name - highway This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Palau **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0005 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0 million km (9.2565%) - Unpaved roads: 0.0001 million km (22.6005%) About 0.0003 million km (68.143%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (3.1715%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Nauru **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0001 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0 million km (23.1658%) - Unpaved roads: 0.0 million km (23.0738%) About 0.0001 million km (53.7604%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (2.5123%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org