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42 results for “road network”
Transportation network system including trails, road construction history, and gates for the Andrews Experimental Forest, 1952-2011
Transportation network locations within the Andrews Experimental Forest. Includes locations of all the roads, trails, and gates within and around the forest. Original road layer was drawn on maps in 1992 and field validated. The road construction history (1952-1990) has been captured as an attribute. Roads were updated in 2004 to include roads that have been abandoned. Gates were field checked in 2004, as well as trail locations. The three data sets were updated after the 2008 LiDAR data was delivered. Roads were digitized on-screen from the bare-earth DEM, and gates were moved to match the new road network. Trails were updated for the 2011 Andrews map update. Many were located through GPS, and new trails were added. The original data is represented, as well as the updated datasets. The road network dataset is in an esri file geodatabase format, and the other datasets are in esri shapefile format, and all are in a zipped file format.
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Resilience of transportation infrastructure networks to road failures
<p>We provide the code used for the analysis that we used in the article <em>Resilience of transportation infrastructure networks to road failures</em>. As the computation for the RoadNetworks that we analysed in this manuscript is quite large, the computational load is quite high. We pre-computed the load values on the cluster and provide the results here. The code for the Figures in the notebooks will therefore not compute the loads, but just load them from files. We provide a <code>My-road-network.ipynb</code> were you can play around with a smaller RoadNetwork, computing everything locally.</p> <p>Published in<br><strong>J. Wassmer, B. Merz, N. Marwan</strong>: Resilience of transportation infrastructure networks to road failures, Chaos, <strong>34</strong>, 013124 (2024). <a href="https://dx.doi.org/10.1063/5.0165839" target="_blank" rel="noopener">DOI:10.1063/5.0165839</a></p>
Dataset for "Thresholds in road network functioning on US Atlantic and Gulf barrier islands"
<p>This dataset accompanies the paper "Thresholds in road network functioning on US Atlantic and Gulf barrier islands" (<a href="https://doi.org/10.31223/X55D1G">https://doi.org/10.31223/X55D1G</a>) and is intended to be used to reproduce the analysis. In this dataset you can find the graphml files generated for each island (103 islands with drivable roads). Each intersection of the island road network is a node, and has an associated elevation and extreme water level value. Also included are the statistics for each network, and a single table with the analysis results for each of the networks with >100 nodes.</p>
Road network graphs for betweenness centrality algorithm
<p>Weighted graph representation of a road network in selected regions. Derived from Open Street Map <a href="https://www.openstreetmap.org/#map=8/49.817/15.478">https://www.openstreetmap.org</a>. The dataset can be used as input for the betweenness centrality algorithm implemented here: <a href="https://code.it4i.cz/ADAS/betweenness">https://code.it4i.cz/ADAS/betweenness</a>.</p> <p><strong>Archive contents</strong><br> The archive contains following folders.</p> <p><strong>CZE</strong><br> Static graphs of three major cities in the Czech Republic (Praha, Brno, Ostrava) and entire Czech road network. Weighted by length of the road segments in metres.</p> <p><strong>PT</strong><br> Static graphs of Lisbon, Porto and entire Portugese road network. Weighted by length of the road segments in metres.</p> <p><strong>Data format</strong><br> Standard UTF-8 encoded CSV files, separated by semicolon with the following columns:<br> <br> <em>id1: (Type: unsigned long) - </em>start node<br> <em>id2: (Type: unsigned long) - </em>end node<br> <em>dist: (Type: unsigned long) - </em>weight of the edge (length in metres, unless described otherwise)<br> <em>edge_id: (Type: unsigned long) - </em>unique edge identifier<br> <br> <br> <br> </p> <p> </p> <p> </p> <p> </p>
Italian TEN-T road network and Hydrogen Refueling Station nodes
<p>This folder contains three ESRI-shapefiles (representing respectively the network edges, the network nodes and the resulting positions found with an optimization framework to site hydrogen refueling stations (HRS) in Italy) and a CSV-file used to characterize the vehicle traffic flow on each origin-destination path on the represented network. </p> <p><strong>network_nodes.shp</strong></p> <p>There are different type of nodes:</p> <ul> <li>NUTS-3 nodes, being the possible origin or destination nodes. They represent the weighted centroid of the NUTS-3 region they refer to. </li> <li>Fuel areas centroids: obtained as the centroids of the dissolved buffer areas of the conventional fuel stations on the road network.</li> <li>Intersection nodes: manually added on the network nodes intersections.</li> <li>Main road entrances.</li> <li>Port nodes.</li> </ul> <p><strong>network_edges.shp</strong></p> <p>The edges represent the Trans-European Network (TEN-T) core and comprehensive layers. Some edges are manually added:</p> <ul> <li>Edges connecting NUTS-3 nodes to the main road network;</li> <li>Ferry edges, which have "road length" equal to 0, as they do not imply fuel consumption for road trucks.</li> </ul> <p><strong>italian_paths_OD_NUTS3_vR1.csv</strong></p> <p>Containing O-D paths thorugh the TEN-T road network from each origin to each destination. Each path is represented as an ordered succession of nodes and of their positions with respect to the origin node. For each path an estimation of the tons and trucks expected on it by 2030 is given. </p> <p><strong>HRS_nodes.shp</strong></p> <p>HRS nodes position and specifications obtained with an optimization framework to serve the national freight transport system if vehicles paths with dist_ROAD > 100 km, Traffic_flow_trucks_2030 >= 2600, initial vehicle range = 300 and maximum vehicle range = 600 km are considered. The fuel cell hydrogen electric vehicles share over the total number of circulating vehicles is assumed equal to 10%. </p> <p> </p> <p> </p>
The spread of a wild plant pathogen is driven by the road network
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Europe Road Network extracted from OpenStreetMap data
<p>The data extracts of Europe region downloaded on 04/07/2020 was used to create this dataset. From this the <strong>highways</strong> tagged as <strong>motorway, trunk, primary, secondary, tertiary, unclassified</strong> and <strong>residential </strong>are selected and the information was saved as line strings. CRS: WGS84 (EPSG:4326)<br> Files available are in parquet and csv format. Please feel free to convert the files in to desired file formats.</p>
Supporting information for: Accounting for the topology of road networks to better explain human-mediated dispersal in terrestrial landscapes
<p><span>Human trade and movements are central to biological invasions worldwide. Human activities not only transport species across biogeographical barriers but also accelerate their post-introduction spread in the landscape. Thus, by constraining human movements, the spatial structure of road networks might greatly affect the regional spread of invasive species. However, few invasion models have accounted for the topology of road networks so far, and its importance for explaining the regional distribution of invasive species remains mostly unexplored.</span><span> To address this issue, we developed a spatially explicit and mechanistic human-mediated dispersal model that accounts and tests for the influence of transport networks on the regional spread of invasive species. Using as a model the spread of the invasive ant <em>Lasius</em> <em>neglectus</em> in the middle Rhône valley (France), we show that accounting for the topology of road networks improves our ability to explain the current distribution of the invasive ant. In contrast, we found that using human population density as a proxy for the frequency of transport events decreases models' performance and might thus not be as appropriate as previously thought. Finally, by differentiating road networks into sub-networks, we show that national and regional roads are more important than smaller roads for explaining spread patterns. Overall, our results demonstrate that the topology of transport networks can strongly bias regional invasion patterns and highlight the importance of better incorporating it into future invasion models. The mechanistic modelling approach developed in this study should help invasion scientists explore how human-mediated dispersal and topography shape invasion dynamics in landscapes. Ultimately, our approach could be combined with demographic, natural dispersal and environmental suitability models to refine spread scenarios and improve invasive species monitoring and management at regional to national scales.</span></p>
Relationship between Road Network Characteristics and Traffic Safety
<p>Corresponding data set for Tran-SET Project No. 17ITSTSA01. Abstract of the final report is stated below for reference:</p> <p>"The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The highrisk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections."</p>
Graph database of the urban road network of Modena
<p>The file contains a dump of the neo4j instance of the road network of the city of Modena. The database contains both the primal graph and the dual graph and integrates traffic volume data and Points Of Interest (POI). The database is generated from Open Street Map data exploiting the code in <a href="https://anonymous.4open.science/r/roadRouting-2D96">this</a> git repository. The git repository shows how to employ the graph database for analysis, routing, and the simulation of road closure scenarios.</p>
Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks
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Data from: Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in mountains
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Supporting information for: Accounting for the topology of road networks to better explain human-mediated dispersal in terrestrial landscapes
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Quantifying the spatial homogeneity of urban road networks via graph neural networks
<p>Publication: Quantifying the spatial homogeneity of urban road networks via graph neural networks, Nature Machine Intelligence, 2022.</p> <p>Publication DOI: 10.1038/s42256-022-00462-y</p> <p>Please refer to https://github.com/jiang719/road-network-predictability.</p>
Supplementary material 1 from: Fernandes N, Ferreira EM, Pita R, Mira A, Santos SM (2022) The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 177-196. https://doi.org/10.3897/natureconservation.47.71864
The effect of habitat encroachment by roads on space use and movement patterns of an endangered vole
Road network with roadscape vectors in Awaji Island, Japan
<p><strong>Summary</strong><br>This dataset contains road network with roadscape vectors in Awaji Island, Japan. The road network data is derived from OpenStreetMap, and it consists of 102,506 road nodes and 212,050 road links in the area of Awaji Island. Roadscape vectors are given to each road link.</p> <p><strong>Road network data</strong><br>A road network is a directed graph G = (V, E), where V is a road node set and E ⊆ V × V is a road link set. A road node vi ∈ V represents an intersection or an end point. A road link ek = (vi, vj) ∈ E represents a directed link from the starting node vi to the ending node vj.</p> <p><strong>Road nodes data</strong><br><code>m_road_nodes_latlng.csv</code><br>Road nodes are contained in the file m_road_nodes_latlng.csv. The number of road nodes is 102,506. Each line corresponds to one node. These lines have the following format as tab delimited: <br><code>node_id, lat, lng</code><br>Here, node_id denotes a road node ID for identifying a road node. lat and lng denote latitude and longitude, respectively.</p> <p><strong>Road links data</strong><br><code>m_road_links_nodes.csv</code><br>Road links are contained in the file m_road_links_nodes.csv. The number of road links is 212,050. Each line corresponds to one link. These lines have the following format as tab delimited: <br><code>link_id, start_node_id, end_node_id</code><br>Here, link_id denotes a road link ID for identifying a road link. start_node_id and end_node_id denote starting node ID and ending node ID, which refer to node_id in the file m_road_nodes_latlng.csv (i.e. the same node ID refers to the same road node across these files), respectively.</p> <p><strong>Roadscape vectors</strong><br>A roadscape vector is defined as a four-dimensional probability vector composed of four kinds of roadscape elements, rural, mountainous, waterside, and urban elements. Each element of the vector denotes the probability of including the roadscape element. Therefore, the sum of values over all elements is 1.</p> <p><strong>Road link vectors data</strong><br><code>m_road_links_vector.csv</code><br>Roadscape vectors of road links are contained in the file m_road_links_vector.csv. Each line corresponds to one link with its roadscape vector. These lines have the following format as tab delimited: <br><code>link_id, rural, mountain, water, urban</code><br>Here, link_id denotes a road link ID, which refer to link_id in the file m_road_lonks_nodes.csv (i.e. the same link ID referes to the same road link across these files). The rural, mountain, water, and urban denote rural, mountainous, waterside, urban elements in its roadscape vector, respectively.</p> <p><strong>Citation<br></strong>To make use of the dataset, please cite the following paper:<strong><br></strong></p> <ol> <li>Koji Kawamata and Kenta Oku. Roadscape-based Route Recommender System using Coarse-to-fine Route Search, In <em>Proceeding of the ACM RecSys Workshop on Recommenders in Tourism (RecTour 2018)</em>, pp.23--27, 2018.</li> <li>Koji Kawamata and Kenta Oku. Roadscape-based route recommender system using coarse-to-fine route search. Journal of Information Processing, 27, pp.392–403. https://doi.org/10.2197/ipsjjip.27.392</li> </ol> <p> </p> <p> </p>
Step-by-step simulation of the ROUTR algorithm on a road-network graph.
<p>This video simulates, step-by-step, the application of the ROUTR algorithm on a small road-network graph.</p>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.