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374 results for “traffic”
Source code for CPBS Report 23UNM03 - Enhancing Collaboration through Web-based Visualization and Analysis of Traffic Crash Data
<p>Python source code for the crash mapping web application.</p>
Athens Car Traffic data SUMO
Open the record for dataset details and reuse information.
Accelerated landing in a stingless bee and its unexpected benefits for traffic congestion
<p>To land, flying animals must simultaneously reduce speed and control their path to the target. While the control of approach speed has been studied in many different animals, little is known about the effect of target size on landing, particularly for small targets that require precise trajectory control. To begin to explore this, we recorded the stingless bee <i>Scaptotrigona depilis</i> landing on their natural hive entrance – a narrow wax tube built by the bees themselves. Rather than decelerating before touchdown as most animals do,<i> S. depilis</i> accelerates in preparation for its high precision landings on the narrow tube of wax. A simulation of traffic at the hive suggests that this counterintuitive landing strategy could confer a collective advantage to the colony by minimising the risk of mid-air collisions and thus of traffic congestion. If the simulated size of the hive entrance increases and if traffic intensity decreases relative to the measured real-world values, 'accelerated landing' ceases to provide a clear benefit, suggesting that it is only a useful strategy when target cross-section is small and landing traffic is high. We discuss this strategy in the context of<i> S. depilis'</i> ecology and propose that it is an adaptive behaviour that benefits foraging and nest defence.</p>
FIGURE 7. Taxon Area Cladograms for A in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 7. Taxon Area Cladograms for A. Mycale immitis (Schmidt, 1870) group; B. Mycale australis (Gray, 1867) group [= M. (Grapelia) spp. sensu Van Soest et al. (2011)]; C. Thrinacophora Ridley, 1885; D. Acarnus Gray, 1867; E. Didiscus Dendy, 1922; F. Placospongia Gray, 1867; G. Rhabderemia Topsent, 1890 and H. Petromica Topsent, 1898. A–E from Van Soest & Hajdu (1997). F. from Nichols and Barnes (2005). G. from Hajdu & Desqueyroux-Faúndez (2008). H. from List-Armitage & Hooper (2002). Areas utilized in the original biogeographic analyses were transformed into Spalding et al.'s (2007) provinces (Table 4). The black circles indicate cladogenetic events separating the areas considered in the TAC-TAC comparisons performed.
FIGURE 6 in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 6. Panbiogeographic map elaborated from both the current distribution of Acanthotetilla spp. and its phylogenetic cladogram (Fig. 5) by the hand method listed in Henderson (1989), subsequently adapted by Hajdu (1995) and in the present study. Arrowheads represent the polarity of each track. Multiple arrowheads in a single track imply several polarities inferred for that track. Letters in square: C = A. celebensis, E = A. enigmatica, G = A. gorgonosclera, H = A. hemisphaerica, R = A. rocasensis, S = A. seychellensis, W = A. walteri.
FIGURE 5 in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 5. Taxon Area Cladogram (TAC) for Acanthotetilla sp. obtained by substituting each terminal taxon by the provinces of Spalding et al. (2007) where they occur. 12, Tropical Northwestern Atlantic. 14, Tropical Southwestern Atlantic. 19, Somali / Arabian. 20, Western Indian Ocean. 29, Tropical Northwestern Pacific. 30, Western Coral Triangle. Spalding et al.'s Realms are identified on the right side. Provinces listed in italics above each node are reconstructions by DIVA 1.1 (Ronquist 1996) of likely ancestral areas.
FIGURE 2 in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 2. Detail of the acanthose diactines of: A—Acanthotetilla seychellensis (RMCA 1410); B—A. rocasensis (MNRJ 6359); C—A. gorgonosclera (ZMA POR 3814); D—A. walteri (UFBA 1896); E—A. celebensis (RMNH POR. 2877); F—A. enigmatica (line art from Van Soest, 1977); G—A. hemisphaerica (line art from Van Soest, 1977); H—C. levantinensis (UFBA 3261); and I—Cinanchyrella kuekenthali (UFBA 2082).
FIGURE 4 in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 4. Most parsimonious cladogram (Nelson consensus = majority-rule consensus with other compatible groupings) for the phylogenetic relationships of Acanthotetilla spp. and outgroups. Numbers above the nodes represent apomorphic, homoplastic and (underlying-) synapomorphic character states. See text for a list of characters and their states utilized in the present analysis. This tree has 29.86 steps, CI = 0.89, HI = 0.10, RI = 0.93 RC = 0.83. Numbers underneath each node are Bootstrap Support for unweighted analyses/ Bootstrap Support for subsequently weighted analyses/ Bremer support for unweighted analyses/ Bremer support for subsequently weighted analyses/ Bayesian credibility (values between paretheses). * nodes present in the Nelson consensus but not in Bootstrap or Bremer trees.
FIGURE 1 in Phylogeny and an integrated biogeography of Acanthotetilla Burton, 1959 (Demospongiae: Spirophorida: Tetillidae): two-way traffic on the peri-African track
FIGURE 1. Perpendicular section (from center to surface) showing the skeleton of: A—Acanthotetilla gorgonosclera (ZMA POR 3814); B—A. celebensis (RMNH POR. 2877); C—A. walteri (UFBA 1896); D—A. rocasensis (MNRJ 6359); E—Cinanchyrella kuekenthali (UFBA 2082); F—C. levantinensis (MNHN-DJV 94) from Vacelet et al. (2007).
Seismic attenuation extraction from traffic signals recorded by a single seismic station (2/3)
<p>This repository contains the raw field data and corresponding codes for the West Coast Highway site in the manuscript entitled "Seismic attenuation extraction from traffic signals recorded by a single seismic station " by Yumin Zhao, Enhedelihai Nilot, Bei Li, Yunyue Elita Li, and Gang Fang. </p>
Seismic attenuation extraction from traffic signals recorded by a single seismic station (1/3)
<p>This repository contains the raw field data and corresponding codes for the NUS exit site in the manuscript entitled "Seismic attenuation extraction from traffic signals recorded by a single seismic station " by Yumin Zhao, Enhedelihai Nilot, Bei Li, Yunyue Elita Li, and Gang Fang. </p>
An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways
<p>Raw data for the paper "An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways".</p>
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
Motorbike taxis drivers pass under a lorry during a traffic jam in Douala
<p>Motorbike taxis drivers pass under a lorry during a traffic jam in Douala</p>
Dataset: bird traffic rate at Bolle di Magadino Natural Reserve, Lake Maggiore (CH)
<p>A 2 year (2021-2022) dataset of bird traffic rate measured with the avian vertical-looking radar BirdScan MR1, installed near the Bolle di Magadino Natural Reserve protected area (Switzerland), an important stop-over site on the northern shore of Lake Maggiore. The dataset includes over 200000 records of hourly traffic rates measured from 0 to 3000 m above ground level, with altitude bins of 100 m.</p>
IoT Emulated Dataset for ICMP/Ping Normal and Malicious Traffic
<p>These datasets are related to Intrusion Detection System, Computer Network Traffic and IoT.</p> <p>These datasets are generated for the purpose of differentiating <strong>ICMP/Ping normal and malicious traffic </strong>that are generated from an embedded device (IoT). The differentiation analysis is done using machine learning.</p> <p>There are three types of files that depend on each module of our research framework. The data generation sequence is as follows:</p> <p>The <strong>pcap files </strong>(network traffic) are generated first, the device used to generate the data is an ESP-01s. Afterwards, the pcap files are transformed into <strong>log files </strong>using the Zeek tool, the log files are then extracted and placed into <strong>CSV files</strong>.</p> <p>The CSV files are labeled and ready for the Machine Learning process.</p> <p> </p> <p>The publication reference for this work is here : https://doi.org/10.1109/ACCESS.2023.3327061</p> <p>The code link: <a href="../badge/latestdoi/619245496">https://zenodo.org/badge/latestdoi/619245496</a></p> <p><strong>This version of the release (0.2.0) is for ping flood with spoofed IPs, however, the previous version (</strong>0.1.0)<strong> is for static IP</strong></p> <p> </p> <p> </p> <p> </p>
Weekly road traffic collision (AXA Mexico) & weekly road traffic deaths
<p><span>Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. </span>Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx).</p> <p><span>Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. </span>Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p).</p>
Facts-Up-Front Versus Traffic-Light Food Labels
ClinicalTrials.gov study NCT01626729. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Impact of Prehospital Trauma Life Support (PHTLS) on Outcome of Traffic Injury
ClinicalTrials.gov study NCT01603537. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Safety and Efficacy of Integrative Korean Medicine Treatment for Elderly After Traffic Accident
ClinicalTrials.gov study NCT07007299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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.