This dataset was used in the experimental evaluation of the MUSE route planning algorithm. It encompasses the Ile-de-France multimodal network. The dataset contains:

1. The raw osm file of the Ile-de-France region from OpenStreetMap.

2. The raw GTFS data for the public transit network.

3. The multimodal graph based on 5 partitions: 100, 200, 300, 400, and 500 cells.

4. The Nondeterministic Finite Automata (NFA) used during the preprocessing and query stages of MUSE.

5. The graph overlay evaluated during the preprocessing stage of MUSE.

To test MUSE and review the details of this dataset, please visit https://github.com/aminefalek/muse

Unlock precise, high-quality Map data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.†Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety eleme..., The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk., , # Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities

https://doi.org/10.5061/dryad.w9ghx3g0j

Description of the data and file structure

For this project’s analysis, the team obtained data from FEMA's National Risk Index, including the Social Vulnerability Index (SOVI).

To estimate SOVI, the team used data from the American Community Survey (ACS) to calculate SOVI at the census block level.

Using the graphs obtained from OpenStreetMap (OSM), the authors estimated the Hansen Accessibility Index (Ai) and the normalized betweenness centrality (BC) for each node in the graph.

The authors estimated the Road Network Performance (RNP) risk at the node level by combining NRI, Ai, and BC. They then grouped the RNP to determine the RNP risk at the regional level and generated the radial histogram. Finally, the authors calculated each ana...

This layer shows the location of traffic impact assessment routes for roadworks in Hong Kong. It is a set of data made available by the Transport Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

Unlock precise, high-quality GIS data covering 46M+ verified locations across North America. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

This information was derived from the 2017 Asset Data Collection. The elevation information associated with the Medians (a continuous asset) delivery was used. The beginning and ending elevations for a section were averaged, rounded to the nearest hundred, then dissolved based on elevation, region, and route.For more information please see the Data Assessment Form. For questions on the data please contact Chris Meredith at cmeredith@utah.gov.To download this data please visit UDOT's Open Data Site.Notes:Service Type - ArcGis Pro on 08Location - 08 in the Complex\UDOT Elevation folder. Document name Elevation.aprxPublished to 08 (Central) in the UDOT Folder. Service Name is Elevation

The European Directive 2002/49/EC on the assessment and management of environmental noise sets out a common approach for all Member States of the European Union to avoid, prevent or reduce as a matter of priority the harmful effects of exposure to noise in the environment. It has been transposed into French law by ordinance, ratified by the Law of 26 October 2005 and is now included in the Environmental Code. This approach is based on noise exposure mapping, population information and implementation of the Environmental Noise Prevention Plan (PPBE) at the local level. Articles L572-1 to L572-11 and R572-1 to R572-11 of the Environmental Code define the competent authorities to adopt noise maps and environmental noise prevention plans. As regards the major road and rail infrastructure of the national network, the noise maps and the PPBEs are adopted by the Prefect, in accordance with the conditions laid down in the circular of 7 June 2007 concerning the drawing up of noise maps and plans for the prevention of environmental noise and by the instruction of 23 July 2008.

In the light of the circular of 7 June 2007 on the preparation of noise maps and environmental noise prevention plans, noise maps are to be drawn up for large infrastructure and in large agglomerations. The following are concerned: — roadways used by more than 8200 vehicles/d — railways with more than 82 train crossings/d — agglomerations with a population of more than 100 000 inhabitants

In Phase I of this NASA SBIR project, Daniel H. Wagner Associates, Inc., designed and demonstrated the feasibility of a system for integrating environmental data into flight planning and execution for Unmanned Air Systems (UAS) in the National Airspace System (NAS). The Weather Aware Route Planning (WARP) system will provide weather-based Indicators and Warnings (I&W) and navigational recommendations for UAS in order to improve their autonomy, safety, and energy efficiency. Using all available environmental and navigational data, WARP will assess environmental impacts to planned/executing flight plans and generate alerts and recommendations for those plans based on expected environmental impacts. Operating in conjunction with existing and emerging mission planners and ground control systems (GCS), WARP will use a combination of rules-based/heuristic and simulation-based approaches to assess environmental impacts to UAS flight plans and provide I&W and recommendations for each UAS to avoid negative environmental impacts and take advantage of positive environmental impacts. WARP will also provide real-time environmental impact assessments during mission execution, assisting ground-based pilots, and eventually UAS autonomous controllers, in performing dynamic re-planning for safer and more efficient flight.

The Washington State Department of Transportation (WSDOT) developed this data set in fulfillment of a grant from the Federal Highway Administration (FHWA) to test a conceptual climate risk assessment model developed for transportation infrastructure. WSDOT applied the model using scenario planning in a series of statewide workshops, using local experts, to create qualitative assessment of climate vulnerability on its assets in each region and mode across Washington. For the purposes of this statewide effort, managed assets were defined as sections of highway or railroad, and whole facilities (Ferry Terminal or Airport). Fourteen workshops engaged experts across all WSDOT regions, state ferries, rail, and aviation. The outcome of each workshop was a subjective evaluation of asset vulnerability agreed upon by participants. This feature class contains the results for state routes. This study assumed 100% probability of climate change impacts previously identified in the University of Washington Climate Impacts Group's 2009 assessment. Types of impacts discussed in the workshops with local experts included: temperature changes, increase in extreme weather events, precipitation changes, sea level rise, fire risk, and high winds. The scientific community's understanding of climate impacts continues to evolve as the models and collective understanding of feedback systems improve. We do not have perfect information about exactly how, when, where, and to what magnitude climate changes will unfold in Washington State. After reviewing the extreme weather events and other impacts projected for their area, workshop participants defined sections of highway, rail, or specific facilities with consideration of the local geology, natural and constructed drainage and hydrology, elevation, slope, land use and operational maintenance issues. Once defined, each corridor or facility was then ranked for two variables: asset criticality and potential impact. Asset criticality (which was defined by the workshop participants) should not be confused with other measures such as highway functional class. 1) How critical is that site or corridor to overall transportation operations and public safety? The following scale guided the qualitative assessment of criticality: a. 1-3 = Low - facility/corridor with low daily traffic, available alternate routes, not part of the National Highway System b. 4-6 = Medium - facility/corridor has low to medium daily traffic, serves as an alternate route of other state corridors or facilities c. 7-10 = High - facility/corridor is an Interstate or other major highway, is considered a lifeline route or is the sole access to a population center or critical facility. 2) How might potential climate changes impact site or corridor operations? The following scale guided the assessment of climate impacts: a. 1-3 = Low - Reduced Capacity: facility/corridor partially open to use and full operations can be restored within 10 days b. 4-6 = Medium - Temporary Operational Failure: Facility/corridor closed for hours or days. Reopening or repair could be completed within 60 days. c. 7-10 = High - Complete Failure: facility/corridor likely to require major repair or rebuild with closures lasting more than 60 days These qualitative rankings for impacts and asset criticality and some general descriptions were captured in spreadsheets that were later used to create GIS layers. This data is intended for use in statewide or regional planning and to assist in adapting maintenance and engineering policies and practices to protect our transportation infrastructure over the coming decades. The rankings here were based on our knowledge and understanding at the time of the study, and should only be taken as a best professional estimate for considering potential conditions that might put people or infrastructure at risk. Current information about projected climate changes and asset use and condition should always be taken into account, especially as time progresses.

Coverage: Percentage of generated routes which are observed using alternative route choice set generation algorithms based on GPS data and Twin Cities regional planning network.

Asia Pacific Digital Maps Market size was valued at USD 19.2 Billion in 2024 and is projected to reach USD 67.8 Billion by 2032, growing at a CAGR of 14.1% from 2026 to 2032.

Asia Pacific Digital Maps Market Overview

In recent years, geospatial information has experienced growth due to its broad range of applications in various sectors and businesses such as risk and emergency management, marketing, urban planning, infrastructure management, resource management (oil, gas, mining), and business planning, logistics, and many others. In the Asia Pacific region, geospatial technologies are utilized for rural and agricultural development. In this region, companies are involved in engineering and construction, mining and manufacturing, insurance, and agriculture.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, we applied photointerpretative observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Finally, map validation occurred prior to the accuracy assessment. Staff from RSGIG conducted a field trip in conjunction with other meetings in Flagstaff, AZ in January 2001 to refine and assess the initial mapping effort. On this trip we collected additional photointerpretative observations and ground-truthed aerial photograph signatures using landmarks and GPS waypoints. Map classes were lumped or split to account for inadequacies in the final photointerpretation. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software and CPRS used ArcCatalogue software to create the FGDC-compliant metadata files attached to the spatial databases and to this report (see Appendix A). The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the classification relevé data coverage created by CPRS, and the accuracy assessment observation data created by CPRS.

This information was derived from the 2017 Asset Data Collection. The grade information collected along with Pavement Condition was used. All values were rounded to the nearest whole number then adjoining tenth-mile features were dissolved according to the rounded grade value.For more information please see the Data Assessment Form. For questions on the data please contact Chris Meredith at cmeredith@utah.gov.To download this data please visit UDOT's Open Data Site.

Emergency Vehicles (EVs) are of considerable significance in saving human lives and property damages. To promote the efficiency of emergency operation, signal preemption control could give priority to EVs heading toward the incident location. On the other hand, providing dynamic and precise route planning for EVs plays an important role in emergency rescue since traffic changes constantly. Furthermore, connected vehicle (CV) technology that incorporates advanced wireless communication technologies, offers a huge potential to promote the efficiency of EVs and maintain smooth traffic flow via collaborative optimization of routes and signals. This study presents a bi-level dynamic emergency route planning system considering signal preemption control, which builds on traffic flow combined with hierarchical bi-layer model predictive control (MPC), for more than one EV under partial CV environment. In this approach, the mobility of EVs is prioritized before decreasing the impact of EVs operation on normal traffic. In the upper layer, a road-level emergency route would be dynamically planned and updated after each time horizon, according to the network-wide traffic flow estimation under diverse CV market penetration ratios through loop detectors and Cellular-Vehicle-to-Everything (C-V2X) communication. In the lower layer, a lane-level emergency route that combined with signal preemption control would be planned to ensure the efficiency of EVs and reduce the adverse impact of signal preemption on normal traffic. In the end, a microscopic simulation environment for a real traffic network is carried out to test the effectiveness of the proposed system. The simulation results indicate that the proposed system provides reliable and practical emergency route planning and signal control services for EVs under different traffic flow conditions.

This dataset contain (1) List of articles resulted from the original search (2) List of articles selected for full text review (3) List of selected articles with key data extracted (4) summary of quality scores (5) quality assessment checklist

The layer contains the public transport route network for the services of the city plan

These data depict Road Segment Centerlines and attributes for roads that are managed and maintained by the National Park Service. Road data are used for many purposes including planning and management, mapping and condition assessment, routing and navigation, public information, emergency response, and research. A current, accurate representation of park roads is needed for national reporting and a variety of mapping requirements at all levels of the National Park Service and the general public. A National-level dataset allows the NPS to communicate a consistent and high-quality roads database to NPS staff, partners, visitors, and entities that produce maps and location-based services of park units. The collection, storage, and management of road-related data are important components of everyday business activities in many Federal and State land-managing agencies, road organizations, and businesses. From a management perspective, road data must often mesh closely with other types of infrastructure, resource, and facility enterprise data. For the public using paper maps, the internet, GPS or other instrumentation, standard data formats enable users to consistently and predictably identify specific trails and a core set of corresponding information. Today, digital road data are a necessity throughout a road data management life-cycle, from road planning through design, construction, operation, and maintenance. Automating, sharing, and leveraging road data through this widely accepted standard can provide a variety of important benefits: Efficiency – creating and gathering road data that are standardized and readily usable. Compatibility – compiling data from one project or discipline that can be compatible with other applications; Consistency – using the same standards, meshing data produced by one organization with that developed by another; Speed – hastening the availability of data through a reduction in duplicative efforts and lowered production costs (Applications can be developed more quickly and with more interoperability by using existing standards-compliant data); Conflict resolution – resolving conflicting road data more easily if compliant to the same standards; Reliability – improving the quality of shared road data by increasing the number of individuals who find and correct errors; and Reusability – allow maximum reuse across agencies and support objectives of EGovernment (E-Gov) initiatives and enterprise architecture.

We studied the habitat selection of pronghorn (Antilocapra americana) during seasonal migration; an important period in an animal's annual cycle associated with broad-scale movements. We further decompose our understanding of migration habitat itself as the product of both broad- and fine-scale behavioral decisions and take a multi-scale approach to assess pronghorn spring and fall migration across the transboundary Northern Sagebrush Steppe region. We used a hierarchical habitat selection framework to assess a suite of natural and anthropogenic features that have been shown to influence selection patterns of pronghorn at both broad (migratory neighborhood) and fine (migratory pathway) scales. We then combined single-scale predictions into a scale-integrated step selection function (ISSF) map to assess its effectiveness in predicting migration route habitat. During spring, pronghorn selected for native grasslands, areas of high forage productivity (NDVI), and avoided human activity (i.e., roads and oil and natural gas wells). During fall, pronghorn selected for native grasslands, larger streams and rivers, and avoided roads. We detected avoidance of paved roads, unpaved roads, and wells at broad spatial scales, but no response to these features at fine scales. In other words, migratory pronghorn responded more strongly to anthropogenic features when selecting a broad neighborhood through which to migrate than when selecting individual steps along their migratory pathway. Our results demonstrate that scales of migratory route selection are hierarchically nested within each other from broader (second-order) to finer scales (third-order). In addition, we found other variables during particular migratory periods (i.e., native grasslands in spring) were selected for across scales indicating their importance for pronghorn. The mapping of ungulate migration habitat is a topic of high conservation relevance. In some applications, corridors are mapped according to telemetry location data from a sample of animals, with the assumption that the sample adequately represents habitat for the entire population. Our use of multi-scale modelling to predict resource selection during migration shows promise and may offer another relevant alternative for use in future conservation planning and land management decisions where telemetry-based sampling is unavailable or incomplete.

These data depict Road Segment Centerlines and attributes for roads that are managed and maintained by the National Park Service. Road data are used for many purposes including planning and management, mapping and condition assessment, routing and navigation, public information, emergency response, and research. A current, accurate representation of park roads is needed for national reporting and a variety of mapping requirements at all levels of the National Park Service and the general public. A National-level dataset allows the NPS to communicate a consistent and high-quality roads database to NPS staff, partners, visitors, and entities that produce maps and _location-based services of park units. The collection, storage, and management of road-related data are important components of everyday business activities in many Federal and State land-managing agencies, road organizations, and businesses. From a management perspective, road data must often mesh closely with other types of infrastructure, resource, and facility enterprise data. For the public using paper maps, the internet, GPS or other instrumentation, standard data formats enable users to consistently and predictably identify specific trails and a core set of corresponding information. Today, digital road data are a necessity throughout a road data management life-cycle, from road planning through design, construction, operation, and maintenance. Automating, sharing, and leveraging road data through this widely accepted standard can provide a variety of important benefits: Efficiency – creating and gathering road data that are standardized and readily usable. Compatibility – compiling data from one project or discipline that can be compatible with other applications; Consistency – using the same standards, meshing data produced by one organization with that developed by another; Speed – hastening the availability of data through a reduction in duplicative efforts and lowered production costs (Applications can be developed more quickly and with more interoperability by using existing standards-compliant data); Conflict resolution – resolving conflicting road data more easily if compliant to the same standards; Reliability – improving the quality of shared road data by increasing the number of individuals who find and correct errors; and Reusability – allow maximum reuse across agencies and support objectives of EGovernment (E-Gov) initiatives and enterprise architecture.

Unlock precise, high-quality Mapping data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our Mapping solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of Mapping Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use Mapping data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage Mapping intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize Mapping insights for disaster preparedness, sustainable city planning, and land-use management.