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374 results for “traffic”
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://zenodo.org/record/3928550">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the original OpenSky paper:</p> <p>Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm.<br> "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research".<br> In<em> Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN)</em>, pages 83-94, April 2014.</p> <p>and the traffic library used to derive the data:</p> <p>Xavier Olive.<br> "traffic, a toolbox for processing and analysing air traffic data."<br> <em>Journal of Open Source Software</em> 4(39), July 2019.</p>
Data from: Chronic traffic noise increases food intake and alters gene expression associated with metabolism and disease in bats
<ol> <li>Anthropogenic noise exposure has deleterious effects on the foraging ecology of many animals. However, the effects of chronic anthropogenic noise on food intake and health condition in wildlife remain largely unknown.</li> <li>We tested whether traffic noise exposure over multiple days would change food intake and would have effects on the health of Asian particoloured bats. We broadcast traffic noise to the bats of two noise-exposure groups (group A, 5 bats; group C, 6 bats) and broadcast silence files to the bats of two control groups (group B, 5 bats; group D, 6 bats) for 12 days. We measured the changes in food intake, body weight, and concentration of faecal triiodothyronine (T3) and thyroxine (T4) in groups A and B. We compared the transcriptional profiles in brain, kidney and liver tissues between bats in groups C and D.</li> <li>The bats exposed to traffic noise had a significantly higher daily food intake and a significantly greater body weight than bats in control group during the period of playback. Faecal T3 and T4 were higher in the noise-exposure group after playback had been on for more than one day.</li> <li>We found a total of 169 differentially expressed genes (DEGs) between noise-exposure and control groups. Some DEGs related to stress response were up-regulated in the bats exposed to noise. The 169 DEGs were mainly enriched in Gene Ontology (GO) terms associated with metabolism, fundamental cellular processes, stress response and immune response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways affected by noise exposure were linked with metabolism, disease, apoptosis, autophagy, phagosome and ribosome.</li> </ol> <p><i>Synthesis and applications</i>. Our results suggest that bats exposed to chronic traffic noise while roosting may need more energetic intake and have a greater motivation to forage than others not exposed to noise, probably as a result of a stress response. Furthermore, chronic traffic noise may increase the risk of metabolic dysregulation, immune disorders, and other diseases. Management measures for reducing noise disturbance, for example, implementing sound barriers, are essential.</p>
Application specific network traffic with specified activities
<p>This dataset contains network traffic of 12 different containerized, isolated applications. It includes headers of network traffic data in the initial two minutes of execution of the application.<br> We have specified each network traffic with an activity that the application was involved in during traffic capturing.<br> </p> <p>------------------------------------<br> Application name, No. of samples, Engaged activity while capturing<br> ------------------------------------<br> Curl, 60, downloading<br> Firefox, 47, browsing<br> mpg123, 99, audio streaming<br> mplayer, 82, audio streaming<br> mpv, 48, video streaming<br> Slack, 58, no chatting (just opening the app and menus)<br> streamlink, 57, video streaming<br> Trello, 46, just opening the app (just opening the app and menus)<br> Vlc, 102, audio streaming<br> w3m, 60, downloading<br> wget, 20, file downloading<br> wgetaudio, 20, file downloading(audio)<br> wgetvideo, 20, file downloading(video)<br> ydm (mplayer + youtube-dl),61, video streaming</p>
HTTP Traffic Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a>, and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format.</p>
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Test dataset for separation of speech, traffic sounds, wind noise, and general sounds
<p>The dataset was generated as part of the paper:<br> Deep Complex U-Net Ensemble for Outdoor Urban Sound Source Separation,<br> K. Arendt, A. Szumaczuk, B. Jasik, P. Masztalski, K. Piaskowski, M. Matuszewski, K. Nowicki, P. Zborowski.</p> <p>It contains various sounds from the Audio Set [1] and spoken utterances from VCTK [2] and DNS [3] datasets.</p> <p>Contents:<br> sr_8k/<br> mix_clean/<br> s1/<br> s2/<br> s3/<br> s4/<br> sr_16k/<br> mix_clean/<br> s1/<br> s2/<br> s3/<br> s4/<br> sr_48k/<br> mix_clean/<br> s1/<br> s2/<br> s3/<br> s4/</p> <p>Each directory contains 512 audio samples in different sampling rate (sr_8k - 8 kHz, sr_16k - 16 kHz, sr_48k - 48 kHz).<br> The audio samples for each sampling rate are different as they were generated randomly and separately.<br> Each directory contains 5 subdirectories:<br> - mix_clean - mixed sources,<br> - s1 - source #1 (general sounds),<br> - s2 - source #2 (speech),<br> - s3 - source #3 (traffic sounds),<br> - s4 - source #4 (wind noise).</p> <p>The sound mixtures were generated by adding s2, s3, s4 to s1 with SNR ranging from -10 to 10 dB w.r.t. s1.</p> <p><br> REFERENCES:</p> <p>[1] Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman,<br> Aren Jansen, Wade Lawrence, R. Channing Moore,<br> Manoj Plakal, and Marvin Ritter, “Audio set: An ontology<br> and human-labeled dataset for audio events,” in<br> Proc. IEEE ICASSP 2017, New Orleans, LA, 2017.</p> <p>[2] Christophe Veaux, Junichi Yamagishi, and Kirsten Mac-<br> Donald, “CSTR VCTK corpus: English multi-speaker<br> corpus for CSTR voice cloning toolkit, [sound],”<br> https://doi.org/10.7488/ds/1994, University of Edinburgh.<br> The Centre for Speech Technology Research<br> (CSTR). 2017.</p> <p>[3] Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra<br> Dubey, Vishak Gopal, Roger Cheng, Ross Cutler,<br> Sergiy Matusevych, Robert Aichner, Ashkan Aazami,<br> Sebastian Braun, Puneet Rana, Sriram Srinivasan, and<br> Johannes Gehrke, “The interspeech 2020 deep noise<br> suppression challenge: Datasets, subjective speech<br> quality and testing framework,” 2020.</p>
Data related to the manuscript entitled "Traffic Data Visualization: A Systematic Literature Review"
<p>Data related to the manuscript entitled "Traffic Data Visualization: A Systematic Literature Review"</p>
"I personally relate it to the traffic light" - Appendix
<p>This upload contains the Auxiliary Materials i.e. surveys and answer datasets for our ACM SAC 2021 paper.<br> <br> Full paper reference:<br> Borce Stojkovski, Gabriele Lenzini, and Vincent Koenig. 2021. “I personally relate it to the traffic light”: a user study on security & privacy indicators in a secure email system committed to privacy by default. In The 36th ACM/SIGAPP Symposium on Applied Computing (SAC ’21), March 22–26, 2021, Virtual Event, Republic of Korea. ACM, New York, NY, USA, Article 4, 10 pages. https://doi.org/10.1145/3412841.3441998 <br> <br> The research is supported by the Luxembourg National Research Fund through grant PRIDE15/10621687/SPsquared, and the project pEp Security SA / SnT “Protocols for Privacy Security Analysis”.</p>
Traffic noise inhibits cognitive performance in a songbird
<p>Noise pollution is commonly associated with human environments and mounting evidence indicates that noise has a variety of negative effects on wildlife. Noise has also been linked to cognitive impairment in humans and because many animals use cognitively-intensive processes to overcome environmental challenges, noise pollution has the potential to interfere with cognitive function in animals living in urban areas or near roads. We experimentally examined how road traffic noise impacts avian cognitive performance by testing adult zebra finches (Taeniopygia guttata) on a battery of foraging tasks in the presence or absence of traffic noise playback. Here we show that traffic noise reduces cognitive performance, including inhibitory control, motor learning, spatial memory, and social learning, but not associative color learning. This study demonstrates a novel mechanism through which anthropogenic noise can impact animals, namely through cognitive interference, and suggests that noise pollution may have previously unconsidered consequences for animals.</p>
Data from: Experimental traffic noise attracts birds during the breeding season
Understanding how anthropogenic disturbance affects animal behavior is challenging because observational studies often involve co-occurring disturbances (e.g., noise, lighting, and roadways), and laboratory experiments often lack ecological validity. During the 2016 and 2017 avian breeding seasons, we investigated the effects of anthropogenic noise and light on the singing and spatial behavior of wild birds by independently manipulating the presence of each type of disturbance at 89 sites in an otherwise undisturbed boreal forest in Labrador, Canada. Each treatment was surrounded by an 8-channel microphone array that recorded and localized avian vocalizations throughout the manipulation. We analyzed the effects of noise and light on the timing of the first vocalizations of each species at each array during the dawn chorus, and on the proximity of the vocalizing birds to the disturbance when those songs were produced. We analyzed all species combined, and then conducted separate analyses for the six most common species: Boreal Chickadee, Dark-eyed Junco, Ruby-crowned Kinglet, Swainson's Thrush, White-throated Sparrow, and Yellow-rumped Warbler. When all species were analyzed together, we found that traffic noise attracted vocalizing birds. There was some evidence that light repelled birds, but this evidence was inconsistent. In our species-specific analyses, Yellow-rumped Warbler sang earlier in response to noise; Swainson's Thrush was attracted to noise and the combination of noise and light, but repelled by light alone. Our study provides some of the first experimental evidence of the independent and combined effects of traffic noise and light on the vocal and spatial behavior of wild birds, and suggests that breeding birds may be attracted to noisy roads where they could be exposed to additional forms of disturbance.
Data from: The journey from traffic offender to severe road trauma victim: destiny or preventive opportunity?
Background: Road trauma is a leading cause of death and injury in young people. Traffic offences are common, but their importance as a risk indicator for subsequent road trauma is unknown. This cohort study assessed whether severe road trauma could be predicted by a history of prior traffic offences. Methodology and Principal Findings: Clinical data of all adult road trauma patients admitted to the Western Australia (WA) State Trauma Centre between 1998 and 2013 were linked to traffic offences records at the WA Department of Transport. The primary outcomes were alcohol exposure prior to road trauma, severe trauma (defined by Injury Severity Score >15), and intensive care admission (ICU) or death, analyzed by logistic regression. Traffic offences directly leading to the road trauma admissions were excluded. Of the 10,330 patients included (median age 34 years-old, 78% male), 1955 (18.9%) had alcohol-exposure before road trauma, 2415 (23.4%) had severe trauma, 1360 (13.2%) required ICU admission, and 267 (2.6%) died. Prior traffic offences were recorded in 6269 (60.7%) patients. The number of prior traffic offences was significantly associated with alcohol-related road trauma (odds ratio [OR] per offence 1.03, 95% confidence interval [CI] 1.02–1.05), severe trauma (OR 1.13, 95%CI 1.14–1.15), and ICU admission or death (OR 1.10, 95%CI 1.08–1.11). Drink-drinking, seat-belt, and use of handheld electronic device offences were specific offences strongly associated with road trauma leading to ICU admission or death—all in a 'dose-related' fashion. For those who recovered from road trauma after an ICU admission, there was a significant reduction in subsequent traffic offences (mean difference 1.8, 95%CI 1.5 to 2.0) and demerit points (mean difference 7.0, 95%CI 6.5 to 7.6) compared to before the trauma event. Significance: Previous traffic offences were a significant risk factor for alcohol-related road trauma and severe road trauma leading to ICU admission or death.
Data from: How traffic facilitates population expansion of invasive species along roads: the case of common ragweed in Germany
1. Because common ragweed (Ambrosia artemisiifolia L., henceforth Ambrosia) has negative effects on human health, it is a common focus for management, which would benefit from a better understanding of the underlying mechanisms by which the species spreads. Road systems are known to be invasion corridors, but the conduit function of vehicles for the rapid spread of Ambrosia along roads and for population extension along roadside verges has not yet been demonstrated convincingly. 2. To quantify the effect of different traffic volumes on the dispersal and population extension of Ambrosia we used two approaches: First, by combining field experiments along roads with records of the seed rain around single plants, we simulated a combined dispersal kernel that revealed the interactions between primary dispersal and traffic-mediated secondary dispersal. Second, we recorded seedling recruitment around isolated roadside populations over two years to determine how traffic-related parameters affect population extension. 3. The longest traffic-mediated dispersal distances exceeded those of primary dispersal by about one order of magnitude. Traffic volume had a significant positive effect on dispersal distances and on the lateral deposition of seeds on the road verge. 4. Seedling recruitment around isolated roadside populations was significantly higher in the driving direction than against, but only at the distance where the major seed rain of traffic-mediated dispersal is to be expected according to the combined dispersal kernel (3-15 m). 5. Synthesis and applications: This study isolates effects of road traffic from confounding mechanisms (e.g. mowing machinery, propagule pressure from infested fields) on common ragweed (Ambrosia artemsiifolia L.) invasions. Results demonstrate traffic-mediated dispersal in Ambrosia invasions as a routine and predictable process that facilitates population extension in the direction of traffic along roadsides, depending on traffic volume. This highlights the importance of prioritizing mowing along high use roads and mowing of isolated populations to prevent seed abscission and further spread of common ragweed Ambrosia.
Data from: Human traffic and habitat complexity are strong predictors for the distribution of a declining amphibian
Invasive species and habitat modification threaten California's native pond-breeding amphibians, including the federally threatened California Red-legged Frog (Rana draytonii). The relative contributions of invasive species, including the American Bullfrog (Lithobates catesbeianus), and of habitat changes to these declines are disputed. I conducted a field study over several years in central California to examine the presence/absence of these two species at 79 breeding ponds to determine the predictive role for occupancy of factors including vegetation, pond characteristics, and measures of human activity. I used a boosted regression tree approach to determine the relative value of each predictor variable. Increased measures of human activity, especially proximity to trails and roads, were the best predictors for the absence of California Red-legged Frogs and California Newts. Historical factors and habitat conditions were associated with the extent and spread of the American Bullfrog. The extent and complexity of aquatic macrophytes and pond surface area were good predictors for the presence of these and other amphibian species. Surprisingly, invasive species played a relatively small role in predicting pond occupancy by the native species. These findings can inform conservation and restoration efforts for California Red-legged Frogs, which apparently persist best in small vegetated ponds in areas of low human disturbance.
Data from: Linking habitat composition, local population densities and traffic characteristics to spatial patterns of ungulate-train collisions
1. Total length of railways worldwide exceeds 1 million kilometres and recent railway development directly impacts wildlife because of animal-train collisions. Few studies, however, have analysed factors driving ungulate-train collisions. 2. We analysed over 3500 ungulate-train collisions including roe deer, red deer, wild boar, and moose collected in 2012-2015 in Poland. We compared train traffic characteristics (e.g. traffic intensity, speed, rail curvature), land-use and habitat characteristics (e.g. share of forests and build-up areas) and local ungulate population densities at collision sites and random sites distributed along the rail network. 3. Forest coverage generally increased, while urban areas decreased ungulate collision risk. Local density of ungulate species was strongly positively related to the relative collision risk in all four ungulate species, but above certain densities, the risk levelled off for all four species. 4. Train speed and train traffic intensity were positively associated with elevated collision risk in all four species, but the latter in a non-linear manner reached an asymptote at the level of ca. 10 trains per day. Rail curvature also increased probability of collisions with roe deer and red deer and possibly also wild boar. 5. Mortality rate of ungulates on railways in Poland is estimated to be 0.13-0.42% of annual hunting bags of studied species assuming that only one individual is killed at each occasion and ignoring undetected collisions. These values are expected to increase in near future due to increasing train speed in Central European countries. 6. Synthesis and applications. Ungulate-train collisions spots are characterised by surrounding forest, rail curvature, high train speed, and a moderate to high train traffic intensity. To reduce collision risk in a cost-effective way, we suggest to prioritise mitigation actions at sections of the railway characterized by those factors, e.g. by fencing and various warning devices. Due to nonlinear correlation between collision risk and population density, reducing density of ungulates will most likely reduce collision risk only marginally, and only in regions of low population densities where collision risk is relatively low anyway.
Data from: Using citizen-collected wildlife sightings to predict traffic strike hotspots for threatened species: a case study on the southern cassowary
Assessing the causal factors underpinning the distribution and abundance of wildlife road-induced mortality can be challenging. This is particularly ubiquitous for rare or elusive species, because traffic strikes occur infrequently for these populations and information about localized abundance, distribution, and movements are generally lacking. Here we assessed if citizen-collected sightings data may serve as a low cost and efficient means of gathering long-term animal road-side presence and road crossing information, which could then be used to assess the causative factors and direct mitigation actions aimed at reducing wildlife traffic strike frequency. We explored this principle using two decades of traffic strike records and citizen-collected sightings of the southern cassowary Casuarius casuarius johnsonii. Roads have bisected the cassowaries' rainforest habitat and despite considerable investment into mitigation strategies for this species, road-induced mortality is considered one of the primary threatening processes affecting the population. Using a Bayesian approach and controlling for spatial autocorrelation with conditional autoregressive (CAR) models, we demonstrate that traffic strikes are primarily a density-dependent process in the southern cassowary. That is, traffic strike clusters occurred along stretches of road where cassowaries were most frequently sighted. There were, however, road stretches where traffic strike frequency was greater than predicted by the number of road-side sightings, illustrating when and where density-independent processes increased the mortality potential for a road-crossing cassowary. Synthesis and applications. This is the first time that citizen-collected sightings data have been used to systematically inform upon the abundance and distribution of wildlife traffic strike. The technique not only predicts where incidents are likely to occur but also helps us to understand the factors responsible for strike clustering. While not a replacement for systematic surveys, we highlight citizen-collected sightings data as a low-cost option when assessing contributing factors to vehicle-induced mortality. Accounting for density-dependent and independent processes will ensure the most effective allocation of resources when implementing wildlife traffic strike mitigation.
Data from: Estimating mobile traffic demand using Twitter
In this letter, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000 km2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and U.K. census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71%-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R2 = 0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 h on the same day and for the same time in the following 2-3 days.
Artifacts for the paper "Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition"
<div> <div> <div>This deposit contains measurement data and additional artifacts pertaining to the "<em>Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition</em>" paper. The deposit is structured as follows:</div> <br> <div>Data pertaining to <strong>RQ1</strong> is found in the <em>baseline-comparison/</em> directory, which contains (1) statistics measured during scenario generation and (2) data analysis results for both our proposed approach (which we name <em>complete</em>) and the baseline <em>Scenic</em> approach, including statistical significance data.</div> <br> <div>Data pertaining to <strong>RQ2-4</strong> and to the following <strong>Discussion</strong> is found in the following three directories.</div> </div> </div> <div> </div> <ul> <li><em>0-generated-scenarios/ </em>contains (1) the generated abstract scenario specifications (i.e. maneuver instance and path region assignments) and (2) concrete scenarios represented in an `xml` format that are executable through the <a href="https://github.com/carla-simulator/scenario_runner">CARLA Scenario Runner</a> framework.</li> <li><em>1-simulation-results/ </em>contains the simulation results for our measurement runs. We include simulation traces, which show the exact position of each actor at each frame, in a human-readable, textual format. We also include result analysis (i.e. pertaining to simulation runtime, outcome, preventive maneuvers, etc.) within `measurements.json` files.</li> <li><em>2-generated-figures/ </em>contains figures derived from the contents of <em>1-simulation-results/</em> (including additional figures not included in the publication).</li> </ul> <div>All the code of the proposed scenario generation approach is implemented as extensions to the <a href="https://github.com/ArenBabikian/concretize">Concretize</a> framework (for scenario generation and analysis), and to the <a href="https://github.com/ArenBabikian/transfuser/tree/complete-gen">Transfuser</a> repository (for simulation). <a href="https://github.com/ArenBabikian/concretize">Concretize</a> is available under the <a href="https://www.eclipse.org/legal/epl-2.0/">Eclipse Public License - v 2.0</a>, while <a href="https://github.com/autonomousvision/transfuser">Transfuser</a> is available under the <a href="https://opensource.org/license/mit">MIT License</a>.</div>
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
Virtual Reality Traces for Traffic Classification
<p>We use two Python scripts located in the 'Python Scripts' folder: one for `Feature Extraction' and another for the `Classification Model'. Initially, we extract features from raw packet traces, which have been stored in the 'Packet Traces' folder. The `Feature Extraction' script generates CSV output files, which become the input for the `Classification Model' script. The resulting input for the training and testing phase is stored in the folders of `Input For Training' and `Input For Test', respectively. Specifically, we have four designated folders: `Packet Traces', `Input For Training', `Input For Test', and `Python Scripts'.</p>
Traffic Cone
Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.