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9 results for “road transportation”
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.
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
Source data for road transportation applications (road surface assessment, authentication of automotive vehicles)
<p>This data set records the driving using an Inertial Measurement Units of 12 different vehicles on the road infrastructure of the European Commission Joint Research Centre.</p> <p>The data set is described more in detail in the paper:</p> <p>Baldini, G.; Geib, F.; Giuliani, R. Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units. <em>Sensors</em> <strong>2019</strong>, <em>19</em>, 5283.</p> <p><a href="https://doi.org/10.3390/s19235283">https://doi.org/10.3390/s19235283</a></p> <p>Please, cite this paper if you use this data set.</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>
Supporting material: Prospective life-cycle assessment of sustainable alternatives for road freight transport
<p><span>This study investigates decarbonization pathways for the road freight transport sector</span><span> </span><span>by evaluating three alternatives to conventional</span><span> </span><span>diesel trucks:</span><span> </span><span>trucks powered by biofuels, battery electric trucks, and fuel cell trucks with hydrogen</span><span>. A prospective life cycle assessment of these options is conducted under two policy scenarios for decarbonization across 12 distinct regions over the century. Employing a cradle-to-grave approach, the assessment covers activities from fuel and electricity production to the end-of-life of truck components. Findings reveal that, in eight of the 12 regions examined, an early transition to battery electric trucks could increase life-cycle greenhouse gas emissions by up to 70% by 2030 compared to the continued use of conventional diesel trucks, underscoring the significance of liquid fuels for short to medium-term decarbonization. However, in the long term, as electricity mixes and hydrogen production are decarbonized, battery electric trucks and hydrogen fuel cell trucks emerge as superior alternatives in all regions, emitting, at least, 29% less greenhouse gases than trucks powered by biofuels, and 45% less than diesel trucks.</span><span> </span><span>The optimal transition from conventional diesel trucks to trucks powered by biofuels and, subsequently, to battery electric trucks and/or hydrogen could avoid 134-204 Gt CO<sub>2-eq</sub> worldwide and prevent a temperature rise of 0.22-0.33°C compared to the diesel-based scenario. This emphasizes the crucial role of appropriate policies for the timely transformation of the road freight transport sector. </span></p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
Dataset for "A Clustering Analysis of Lebanese Adaptive Driving Behaviors in Response to Road Complexity" By Kobeissy et al. Submitted to The Open Transportation Journal
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CAUSING FACTORS OF ROAD TRANSPORT INCIDENTS IN TRAFFIC
Open the record for dataset details and reuse information.
urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM, and TfL public transport timetables, for modelling in London, UK
<h2><strong>Files included in this archive</strong></h2> <p><strong>Documentation</strong></p> <ul> <li> urbisphere_London_transport_database.pdf</li> </ul> <p><strong>JSON Database files</strong> (JSON: <a href="https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml">https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml</a> (<em>last accessed: 30/3/2024</em>))</p> <ul> <li>driving_transport.json <ul> <li>Database of driving routes</li> </ul> </li> <li>cycling_transport.json <ul> <li>Database of cycling routes</li> </ul> </li> <li>walking_transport.json <ul> <li>Database of walking routes</li> </ul> </li> <li>public_transport.json <ul> <li>Database of public transport routes</li> </ul> </li> </ul> <p><strong>Python 3.9 Code</strong></p> <ul> <li>London_travel_dictionaries.py <ul> <li>to create databases</li> </ul> </li> <li>assign_speed_limits.py <ul> <li> to assign OSM speed limits to each road in GLA</li> </ul> </li> <li>reduce_sub_services.py <ul> <li> to group transport routes within the TfL timetables</li> </ul> </li> </ul> <h2>Data purpose</h2> <p>This dataset contains transport routes for walking, driving, cycling, and public transport (train, tube, and bus) within the Greater London (GLA), which can be used for simulations of human behaviour and movement, for example using agent-based models (e.g. Capel-Timms et al. 2021, McGrory et al. 2024b).</p> <h3><em>Associated publications</em></h3> <ul> <li>Hertwig et al. 2024b: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863</li> <li>Hertwig et al. 2024c: urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset].. Zenodo. https://doi.org/10.5281/zenodo.10889841</li> <li>McGrory et al. 2024a: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation].. Zenodo. https://doi.org/10.5281/zenodo.10889900</li> </ul> <div> <div> </div> <div> <p> </p> </div> </div>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.