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374 results for “traffic”
Heavy migration traffic and bad weather are a dangerous combination: Bird collisions in New York City
<p>Bird-building collisions account for 365-988 million bird fatalities every year in the United States alone. Understanding conditions that heighten collision risk is critical to developing effective strategies for reducing this source of anthropogenic bird mortality. Meteorological factors and regional migration traffic may influence collision rates but also may be difficult to disentangle from other effects. We used 5 years of bird collision counts in New York City to examine the influence of nocturnal weather conditions and bird migration traffic rates on collisions with buildings during spring and fall. We found that seasonally unfavorable winds and conditions that impede visibility are important factors that influence rates of bird-building collisions during both seasons. Specifically, northerly and westerly winds and low visibility in the spring, and southerly and westerly winds and low cloud ceiling height in the fall are associated with higher collision risks. Generally, these weather variables associated most strongly with increased collisions when nocturnal bird migration traffic was high, with the exception of low visibility in spring, which was predicted to triple collision rates compared to high visibility, independent of bird migration traffic. Although legislation to turn off unnecessary nocturnal lighting for the entirety of the migration seasons may be an ultimate goal, a proximate goal invaluable for reducing collisions will be predicting which nights will be of highest risk and using this information to determine when mitigation efforts could be most effective.</p>
Acoustic parameters of Phyllodytes luteolus in conditions of low and high noise from vehicle traffic
<p>Acoustic communication in animals can be affected by multiple biotic (intra and interspecific) and abiotic (e.g., wind and rain) natural noises. In addition, human beings produce additional novel sources of noise, which can reduce or inhibit the reception of acoustic signals by conspecifics, leading to behavioral changes. In this study, we investigated whether sound of conspecifics and road noise additively affect the acoustic parameters of the advertisement call of males of a Yellow Heart-tongued Frog (<em>Phyllodytes luteolus</em>). We hypothesized that males that vocalize in larger choruses (many males) and in areas close to highways (anthropic noise) will increase their temporal and spectral acoustic parameters, respectively, to avoid acoustic signal masking. We recorded the vocalizations of 38 males in environments close (N=18) to and distant (N=20) from highways in different social contexts (many or few individuals in the chorus). Contrary to our expectation, the results indicated that individuals exposed to road noise had lower dominant frequency calls than those from natural areas, and that the density of males in the chorus had no influence on the acoustic parameters. Furthermore, we found a positive relationship between body size and intensity, indicating that larger individuals can emit calls that can reach greater distances. The advertisement call of <em>Phyllodytes luteolus</em> has a high dominant frequency, with little overlap with the frequency of anthropic noises (roads), which may explain its presence and reproductive success of this species in bromeliads from urbanized areas.</p>
NGII Data Set for Black Ice Traffic Accident Prediction
<p><a href="../api/records/10863284/draft/files/NGII%20Data%20Set%20for%20Black%20Ice%20Traffic%20Accident%20Prediction.zip/content" target="_blank" rel="noopener noreferrer">Title: NGII Data Set for Black Ice Traffic Accident Prediction</a></p> <p>This dataset has been processed for scholarly purposes, utilizing data provided by the National Geographic Information Institute of Korea.</p> <p><Reference></p> <p>National Geographic Information Institute. (n.d.). National Land Information Platform. Retrieved from https://map.ngii.go.kr/ms/map/NlipMap.do</p>
Translucent Event Logs based on the Road Traffic Fine Management Event Log and the Inductive Miner - infrequent
<p>The translucent events logs in this file are based on the Road Traffic Fine Management Event Log (https://data.4tu.nl/articles/dataset/Road_Traffic_Fine_Management_Process/12683249) published by Massimiliano de Leoni and Felix Mannhardt. The folders' name specify the threshold setting of the Inductive Miner - infrequent (0.4, 0.6, and 0.8). Details on the generation can be found in https://doi.org/10.1007/978-3-031-70396-6_9.</p>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
Open the record for dataset details and reuse information.
2021 DATA SET OF TRAFFIC COUNTES BASED ON CITIZEN SENSORS TO MONITOR DAILY TRAFFIC SOUTHERN INNER BYPASS RING IN LJUBLJANA
<p>Traffic counters for Southern inner bypass ring in Ljubljana, Slovenia. Corresponding roads: Aškerčeva road, Zoisova road, Karlovška road and Roška road.</p>
Atmospheric pollutant concentrations and leaf chemistry variables collected along gradients of median household income and traffic density in Salt Lake Valley, UT
<p>We utilize traffic density and <i>Convolvulus arvensis </i>leaf chemistry<i> </i>to understand spatial patterns linking atmospheric pollution and household income in the Salt Lake City metropolitan area, USA. We hypothesize that traffic density will explain variation in atmospheric NO<sub>x</sub> and O<sub>3</sub> concentrations. In addition, we expect foliar <span>%N and nitrogen isotope ratios (</span>δ<sup>15</sup>N) to be elevated and carbon isotope ratios (δ<sup>13</sup>C) to be depleted on high traffic density roads<span>. These hypotheses were supported: we found the</span><span> highest</span><span> NO<sub>x</sub> and lowest O<sub>3</sub> concentrations on high-traffic density roads. Also, NO<sub>x</sub> concentrations were higher in low-income neighborhoods. Low-income neighborhoods contained a greater density of high-traffic roads than high-income neighborhoods. Foliar %N was highest in low-income neighborhoods and was correlated with NO<sub>x</sub> and O<sub>3</sub></span>, while δ<sup>15</sup>N was more depleted with increasing O<sub>3</sub>. These findings indicate that the distribution of high-traffic density roads plays a significant role in inequities in pollution exposure between high- and low-income neighborhoods, which is reflected in leaf chemistry.</p>
Traffic images captured from UAVs for use in training Machine Vision Algorithms for traffic management
<p><strong>If you use this dataset please cite this paper: Bemposta Rosende, S.; Ghisler, S.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management. Data 2022, 7, 53. https://doi.org/10.3390/data7050053</strong></p> <p>A dataset of road traffic images taken from unmanned aerial vehicles (UAV) with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. </p> <p>Dataset is available and accessible in order to improve the performance of road traffic vision and management systems due to the lack of resources in this specific domain. The full description of the characteristics of the dataset, as well as its components and format, can be found here: <a href="https://www.mdpi.com/2306-5729/7/5/53">https://www.mdpi.com/2306-5729/7/5/53</a></p> <p>The dataset format is YOLO. Relevant data about the dataset:</p> <table> <tbody> <tr> <td> <p><strong>Scenes</strong></p> </td> <td> <p><strong>Frames</strong></p> </td> <td> <p><strong>Targets</strong></p> </td> <td> <p><strong>Cars</strong></p> </td> <td> <p><strong>Motorbikes</strong></p> </td> </tr> <tr> <td> <p>Regional road</p> </td> <td> <p>4.500</p> </td> <td> <p>24.858</p> </td> <td> <p>14.577</p> </td> <td> <p>10.281</p> </td> </tr> <tr> <td> <p>Urban intersection</p> </td> <td> <p>2.462</p> </td> <td> <p>10.759</p> </td> <td> <p>10.759</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Rural road</p> </td> <td> <p>1.292</p> </td> <td> <p>746</p> </td> <td> <p>746</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Split roundabout</p> </td> <td> <p>2.297</p> </td> <td> <p>3.107</p> </td> <td> <p>3.107</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Roundabout (Far)</p> </td> <td> <p>1.814</p> </td> <td> <p>71.819</p> </td> <td> <p>64.844</p> </td> <td> <p>6.975</p> </td> </tr> <tr> <td> <p>Roundabout (Near)</p> </td> <td> <p>3.997</p> </td> <td> <p>4.4039</p> </td> <td> <p>43.569</p> </td> <td> <p>470</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>15.070</strong></p> </td> <td> <p><strong>155.328</strong></p> </td> <td> <p><strong>137.602</strong></p> </td> <td> <p><strong>17.726</strong></p> </td> </tr> </tbody> </table>
Behavior of riparian Iguana iguana: Cano Palma boat traffic study
<p>Although frequently studied in captivity, less is known about wild <em>Iguana iguana</em>. Herein behavior of riparian <em>I. iguana </em>was collected alongside climate, vigilance, and boat traffic data. Observations showed boat tourism potentially affects <em>I. iguana</em> negatively. We recommend minor changes by the ecotourism industry to alleviate visitor pressure on <em>I. iguana</em>.</p>
Physiological responses of narwhals to anthropogenic noise: a case study with seismic airguns and vessel traffic in the Arctic
<p>Limited polar geographical range, narrowly defined migratory routes, and deep-diving behaviors make narwhals exceptionally vulnerable to anthropogenic disturbances including oceanic noise. Although behavioral studies indicate marked responses of cetaceans to disturbance, the link between fear reactions and possible injury from noise exposure is limited for most species.</p> <p>To address this, we deployed custom-made heart rate-accelerometer-depth recorders on 13 adult narwhals in Scoresby Sound, East Greenland across a five-year period (2014-2018). Physiological responses of the cetaceans were monitored in the absence (n = 13 animals) or presence (n = 2 animals across 3 acoustic events) of experimentally directed, seismic airgun pulses and associated vessels (full volume source level = 241 dB re 1 μPa-m).</p> <p>We found that anthropogenic noise resulted in marked cardiovascular, respiratory and locomotor reactions by two narwhals exposed to seismic pulses across three acoustic events. The general behavioral response to seismic and vessel noise included an 80% reduction in the duration of gliding during dive descents by seismic-exposed narwhals compared to controls, and the prolongation of high-intensity activity (ODBA > 0.20 g) with elevated stroke frequencies exceeding 40 strokes per minute. Noise exposure also resulted intense (< 10 bpm) bradycardia that was decoupled from stroking frequency. This decoupling instigated increased variability in heart rate, with the heart switching rapidly between bradycardia and exercise tachycardia during noise exposure. Maximum respiratory frequency following seismic exposure, 12 breaths.min<sup>-1</sup>, was 1.5 times control levels.</p> <p>Overall, the effect of seismic/ship noise exposure on wild narwhals was a 2.0 – 2.2-fold increase in the energetic cost of diving, which paradoxically occurred during suppression of the cardiac exercise response. This unusual relationship between diving heart rate and exercise intensity represents a new metric for characterizing the level of fear reactions of wild marine mammals exposed to different environmental stressors. Together, the multi-level reactions to anthropogenic noise by this deep-diving cetacean demonstrated how a cascade of effects along the entire oxygen pathway could challenge physiological homeostasis especially if disturbance is prolonged.</p>
The dataset of "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow"
<p>There are the datasets of the research named "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow".</p>
Driving Traffic to Institutional Repositories: How Search Engine Optimization can Increase the Number of Downloads from IR
<p>The success of institutional repositories (IR) is measured in large part by the number of file downloads they sustain. Most traffic to IR is referred by Internet search engines, but referrals are hindered when IR are not properly optimized for search engine harvesting and indexing, leading to low visitation and downloads. This presentation will discuss search engine optimization techniques, especially for Google Scholar, which can be responsible for the majority of referrals that result in IR file downloads. The presentation will also introduce a new web service called RAMP (Repository Analytics & Metrics Portal) that accurately counts file downloads from IR and requires no installation or training.</p>
Supplementary material 1 from: Niemi M, Matala J, Melin M, Eronen V, Järvenpää H (2015) Traffic mortality of four ungulate species in southern Finland. In: Seiler A, Helldin J-O (Eds) Proceedings of IENE 2014 International Conference on Ecology and Transportation, Malmö, Sweden. Nature Conservation 11: 13–28. https://doi.org/10.3897/natureconservation.11.4416
Annual trends in population size and collisions: Explanation note: Annual trends in population size, harvest, and collisions.
Supplementary material 2 from: Niemi M, Matala J, Melin M, Eronen V, Järvenpää H (2015) Traffic mortality of four ungulate species in southern Finland. In: Seiler A, Helldin J-O (Eds) Proceedings of IENE 2014 International Conference on Ecology and Transportation, Malmö, Sweden. Nature Conservation 11: 13–28. https://doi.org/10.3897/natureconservation.11.4416
Contingency tables used in the analysis of collision and traffic mortality rates: Explanation note: Contingency tables.
Estimation of the road traffic sound levels based on Non-Negative Matrix Factorization dataset
<p>Sound database to compute the NonnegMatrixFact experience (download the .m files here: https://github.com/jean-remyGloaguen/article2017EstimationAmbiance) dedicated to the estimation of the traffic sound level on urban sound mixtures.</p> <p>This sound database includes two subfolders: <em>ambiance</em> and <em>dictionary</em></p> <p><em>- ambiance </em>folder includes 6 sub-corpus (<em>alert</em>, <em>animals, climate, human, mechanics</em> and <em>transportation</em>). In each sub-corpus, 50 sound mixtures are available and divided into two files: one dedicated to the traffic signal, the other to the interfering signal.</p> <p><em>- dictionary </em>folder includes the isolated sounds dedicated to the dictionary learning for NMF</p>
Simulated performance data: MILC, LAMMPS, and uniform random traffic patterns on 72-ndoe dragonfly network
<p>Data generated from an old, private fork of the CODES simulation toolkit: https://github.com/codes-org/codes</p> <p>Data dictionary: https://github.com/kevinabrown/codes/wiki/Dragonfly-Dally-DEBUG-Metrics</p>
Learn from Human-driving Accidents to Attack Autonomous Driving_Practical Traffic Flow Attacks on Decision-making
<p>We provide some demo videos of attack patterns</p>
Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic
<h1><strong>Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic</strong></h1> <p>The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings."</p> <p>This dataset provides a collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and the aircraft traffic and GPS information calculated and extracted from the OpenSky Trino ADS-B database.</p> <p>The FAA_and_ICAO_locations file includes 21,382 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes file, comprising 5,878,275 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points.</p> <p>The GPS_Missing_Coordinates file, with 53,232 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,160 and 234,205 records respectively, provide detailed information on NOTAM areas, including geographic areas, active periods, and categories. This allows for an analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations.</p> <h1><strong>Summary Table</strong></h1> <table> <tbody> <tr> <td> <p><strong>Category</strong></p> </td> <td> <p><strong>File Names</strong></p> </td> <td> <p><strong>Total Records</strong></p> </td> <td> <p><strong>Columns</strong></p> </td> </tr> <tr> <td> <p><strong>FAA and ICAO Locations</strong></p> </td> <td> <p>FAA_and_ICAO_locations.csv</p> <p>FAA_and_ICAO_locations.dpkg</p> </td> <td> <p>21,382</p> </td> <td> <p>WKT, id, fid, Location_ID, ICAO_ID, IATA_ID, FAA_Location_Code, Facility_Type, Facility_Name, FAA_New_Location_Code, Coordinates, lat, lon, Region, Country_Code, Country, State_Id, State_Name, City, Location, Effective_Date, Site_Id, ADO, ARTCC_Id, ARTCC_Computer_ID, ARTCC_Name, Tie_In_FSS_Id, Tie_In_FSS_Name, NOTAM_Facility_Id, NOTAM_Service</p> </td> </tr> <tr> <td> <p><strong>Flights per Hour per Grid</strong></p> </td> <td> <p>Flights_per_Hour_per_Grid-2023.csv</p> <p>Flights_per_Hour_per_Grid-2023.dpkg</p> </td> <td> <p>74,219,036</p> </td> <td> <p>grid_id, hour, movement_count, geometry</p> </td> </tr> <tr> <td> <p><strong>GPS Jumps from Routes</strong></p> <p><strong>(possible spoofing)</strong></p> </td> <td> <p>GPS_Jumps_from_Routes-2023.csv</p> <p>GPS_Jumps_from_Routes-2023.dpkg</p> </td> <td> <p>5,878,275</p> </td> <td> <p>WKT, id, fid, icao24, callsign, time_before_spoofing, time_of_spoofing, distance, time_difference, speed_m_s, time_start, time_end</p> </td> </tr> <tr> <td> <p><strong>GPS Missing Coordinates</strong></p> <p><strong>(possible jamming)</strong></p> </td> <td> <p>GPS_Missing_Coordinates-2023.csv</p> <p>GPS_Missing_Coordinates-2023.dpkg</p> </td> <td> <p>53,232</p> </td> <td> <p>WKT, id, icao24, callsign, null_start_time, null_end_time, time_of_previous_not_null_coords, time_of_next_not_null_coords, between_coords_distance_m, null_duration_seconds, between_coords_duration_seconds, avg_nic, min_nic, max_nic, start_time, end_time, start_y, end_x, end_y, start_x</p> </td> </tr> <tr> <td> <p><strong>NOTAM ICAO GPS</strong></p> </td> <td> <p>NOTAM_ICAO_GPS-2023.csv</p> <p>NOTAM_ICAO_GPS-2023.dpkg</p> </td> <td> <p>30,160</p> </td> <td> <p>WKT, id, fid, notam_id, category_name, coordinates_center, radius_nm, radius_mod_nm, notam_number, accountability, location_id, icao_id, domestic_text, icao_text, type, category_id, time_start, time_end</p> </td> </tr> <tr> <td> <p><strong>NOTAM USA</strong></p> </td> <td> <p>NOTAM_USA-2023.csv</p> <p>NOTAM_USA-2023.dpkg</p> </td> <td> <p>234,205</p> </td> <td> <p>WKT, id, fid, notam_id, category_name, is_circle, coordinates_polygon, coordinates_center, radius_nm, faa_location_code, is_faa_location, location_id, is_restricted_area, restricted_area_id, restricted_area_code, category_id, message, notam_number, notam_accountability, moa, type, time_start, time_end</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <h1><strong>Details</strong></h1> <h2><strong>1. FAA_and_ICAO_locations.csv </strong>and <strong>FAA_and_ICAO_locations.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 21,382</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>Location_ID</strong>: Identifier for the location.</li> <li><strong>ICAO_ID</strong>: ICAO (International Civil Aviation Organization) identifier.</li> <li><strong>IATA_ID</strong>: IATA (International Air Transport Association) identifier.</li> <li><strong>FAA_Location_Code</strong>: FAA location code.</li> <li><strong>Facility_Type</strong>: Type of facility (e.g., airport, heliport).</li> <li><strong>Facility_Name</strong>: Name of the facility.</li> <li><strong>FAA_New_Location_Code</strong>: New location code by FAA.</li> <li><strong>Coordinates</strong>: Coordinates of the location.</li> <li><strong>lat</strong>: Latitude of the location.</li> <li><strong>lon</strong>: Longitude of the location.</li> <li><strong>Region</strong>: Geographical region of the location.</li> <li><strong>Country_Code</strong>: Country code of the location.</li> <li><strong>Country</strong>: Country name of the location.</li> <li><strong>State_Id</strong>: State identifier.</li> <li><strong>State_Name</strong>: Name of the state.</li> <li><strong>City</strong>: City name.</li> <li><strong>Location</strong>: General location information.</li> <li><strong>Effective_Date</strong>: Effective date of the record.</li> <li><strong>Site_Id</strong>: Site identifier.</li> <li><strong>ADO</strong>: Airport District Office.</li> <li><strong>ARTCC_Id</strong>: ARTCC (Air Route Traffic Control Center) identifier.</li> <li><strong>ARTCC_Computer_ID</strong>: ARTCC computer identifier.</li> <li><strong>ARTCC_Name</strong>: Name of the ARTCC.</li> <li><strong>Tie_In_FSS_Id</strong>: Tie-in Flight Service Station identifier.</li> <li><strong>Tie_In_FSS_Name</strong>: Name of the Tie-in Flight Service Station.</li> <li><strong>NOTAM_Facility_Id</strong>: NOTAM (Notice to Airmen) facility identifier.</li> <li><strong>NOTAM_Service</strong>: Indicates if NOTAM service is available (Y/N).</li> </ul> </ul> <h2><strong>2. Flights_per_Hour_per_Grid-2023.csv </strong>and <strong>Flights_per_Hour_per_Grid-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 74,219,036</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>grid_id</strong>: Identifier for the grid.</li> <li><strong>hour</strong>: Timestamp for the hour.</li> <li><strong>movement_count</strong>: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023.</li> <li><strong>geometry</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> </ul> </ul> <h2><strong>3. GPS_Jumps_from_Routes-2023.csv </strong>and <strong>GPS_Jumps_from_Routes-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 5,878,275</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>icao24</strong>: ICAO 24-bit aircraft address.</li> <li><strong>callsign</strong>: Callsign of the aircraft.</li> <li><strong>time_before_spoofing</strong>: Timestamp before the spoofing event.</li> <li><strong>time_of_spoofing</strong>: Timestamp of the spoofing event.</li> <li><strong>distance</strong>: Distance of the jump in meters.</li> <li><strong>time_difference</strong>: Time difference between two coordinates in seconds.</li> <li><strong>speed_m_s</strong>: Speed in meters per second.</li> <li><strong>time_start</strong>: Start time of the record.</li> <li><strong>time_end</strong>: End time of the record.</li> </ul> </ul> <h2><strong>4. GPS_Missing_Coordinates-2023.csv </strong>and <strong>GPS_Missing_Coordinates-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 53,232</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>icao24</strong>: ICAO 24-bit aircraft address.</li> <li><strong>callsign</strong>: Callsign of the aircraft.</li> <li><strong>null_start_time</strong>: Start time of missing GPS coordinates.</li> <li><strong>null_end_time</strong>: End time of missing GPS coordinates.</li> <li><strong>time_of_previous_not_null_coords</strong>: Time of the last known good GPS coordinates before the null period.</li> <li><strong>time_of_next_not_null_coords</strong>: Time of the first known good GPS coordinates after the null period.</li> <li><strong>between_coords_distance_m</strong>: Distance between the previous and next known good coordinates in meters.</li> <li><strong>null_duration_seconds</strong>: Duration of the null period in seconds.</li> <li><strong>between_coords_duration_seconds</strong>: Duration between the previous and next known good coordinates in seconds.</li> <li><strong>avg_nic</strong>: Average Navigation Integrity Category (NIC) during the period.</li> <li><strong>min_nic</strong>: Minimum NIC during the period.</li> <li><strong>max_nic</strong>: Maximum NIC during the period.</li> <li><strong>start_time</strong>: Human-readable start time of the null period.</li> <li><strong>end_time</strong>: Human-readable end time of the null period.</li> <li><strong>start_y</strong>: Latitude of the start point.</li> <li><strong>end_x</strong>: Longitude of the end point.</li> <li><strong>end_y</strong>: Latitude of the end point.</li> <li><strong>start_x</strong>: Longitude of the start point.</li> </ul> </ul> <h2><strong>5. NOTAM_ICAO_GPS-2023.csv </strong>and <strong>NOTAM_ICAO_GPS-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 30,160</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>notam_id</strong>: NOTAM identifier.</li> <li><strong>category_name</strong>: Name of the NOTAM category.</li> <li><strong>coordinates_center</strong>: Center coordinates of the NOTAM area.</li> <li><strong>radius_nm</strong>: Radius in nautical miles.</li> <li><strong>radius_mod_nm</strong>: Modified radius in nautical miles.</li> <li><strong>notam_number</strong>: NOTAM number.</li> <li><strong>accountability</strong>: Accountability of the NOTAM.</li> <li><strong>location_id</strong>: Location identifier.</li> <li><strong>icao_id</strong>: ICAO identifier.</li> <li><strong>domestic_text</strong>: Text of the NOTAM for domestic purposes.</li> <li><strong>icao_text</strong>: Text of the NOTAM for ICAO purposes.</li> <li><strong>type</strong>: Type of NOTAM.</li> <li><strong>category_id</strong>: Identifier for the NOTAM category.</li> <li><strong>time_start</strong>: Start time of the NOTAM.</li> <li><strong>time_end</strong>: End time of the NOTAM.</li> </ul> </ul> <h2><strong>6. NOTAM_USA-2023.csv </strong>and <strong>NOTAM_USA-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 234,205</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>notam_id</strong>: NOTAM identifier.</li> <li><strong>category_name</strong>: Name of the NOTAM category.</li> <li><strong>is_circle</strong>: Indicates if the NOTAM area is a circle (1) or not (0).</li> <li><strong>coordinates_polygon</strong>: Coordinates of the polygon vertices.</li> <li><strong>coordinates_center</strong>: Center coordinates of the NOTAM area.</li> <li><strong>radius_nm</strong>: Radius in nautical miles.</li> <li><strong>faa_location_code</strong>: FAA location code.</li> <li><strong>is_faa_location</strong>: Indicates if it is an FAA location (1) or not (0).</li> <li><strong>location_id</strong>: Location identifier.</li> <li><strong>is_restricted_area</strong>: Indicates if it is a restricted area (1) or not (0).</li> <li><strong>restricted_area_id</strong>: Restricted area identifier.</li> <li><strong>restricted_area_code</strong>: Code for the restricted area.</li> <li><strong>category_id</strong>: Identifier for the NOTAM category.</li> <li><strong>message</strong>: NOTAM message.</li> <li><strong>notam_number</strong>: NOTAM number.</li> <li><strong>notam_accountability</strong>: NOTAM accountability.</li> <li><strong>moa</strong>: Military Operations Area (MOA) identifier.</li> <li><strong>type</strong>: Type of NOTAM.</li> <li><strong>time_start</strong>: Start time of the NOTAM.</li> <li><strong>time_end</strong>: End time of the NOTAM.</li> </ul> </ul> <p> </p> <p>Note that all the files are zipped as CSV. The GPKG version is also available where geographical information is present.</p> <p>Due to the size of the file <strong>Flights_per_Hour_per_Grid-2023</strong>, the command line program ogr2ogr may be the best choice to upload the data into a database. Below is an example of a SQL script and a command to upload this file into a PostgreSQL table. Update the placeholders your_DB_table, your_DB_name, your_DB_user, your_DB_password, your_DB_hostname with the actual information.</p> <p>CREATE TABLE your_DB_table (grid_id TEXT, date TIMESTAMP, movement_count INTEGER, geometry GEOMETRY(POLYGON, 4326));</p> <p>"C:\Program Files\QGIS 3.36.2\bin\ogr2ogr" -f "PostgreSQL" PG:"dbname=your_DB_name user=your_DB_user password=your_DB_password host=your_DB_hostname port=5432" C:\Flights_per_Hour_per_Grid.gpkg -nln your_DB_table -a_srs EPSG:4326 -dim 2 -progress -append</p> <h1><strong>Author</strong></h1> <p>Eugene Pik</p> <p><a href="https://orcid.org/0000-0001-6296-919X">https://orcid.org/0000-0001-6296-919X</a></p> <p><a href="https://www.linkedin.com/in/eugene/">https://www.linkedin.com/in/eugene/</a></p> <p>eugene.pik@mevocopter.com</p> <h1><strong>DOI</strong></h1> <p><a href="https://doi.org/10.5281/zenodo.11411991">https://doi.org/10.5281/zenodo.11411991</a></p> <h1><strong>References</strong></h1> <p><strong>Following references were used to update NOTAMs with WKT polygons:</strong></p> <p>airport-data.com. (n.d.). <em>USA airports by FAA code</em>. https://www.airport-data.com/usa-airports/faa-code/A.html</p> <p>DoD. (2019). <em>Flight information publication area planning special use airspace</em>. NATIONAL GEOSPATIAL-INTELLIGENCE AGENCY. https://www.cnatra.navy.mil/assets-global/docs/area-planning-1A-20190815.pdf</p> <p>FAA. (n.d.-a). <em>Airport Data and Information Portal</em>. https://adip.faa.gov/agis/public/#/airportSearch/advanced</p> <p>FAA. (n.d.-b). <em>US ICAO location finder</em>. https://www.notams.faa.gov/common/icao/USA.html</p> <p>FAA. (2017a, September 30). <em>Encodes/decodes—Aeronautical data</em> [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/loc_id_search/Encodes_Decodes/</p> <p>FAA. (2017b, October 12). <em>Aeronautical information manual—Official guide to basic flight Information and ATC procedures</em>. https://www.faa.gov/air_traffic/publications/media/AIM_Basic_dtd_10-12-17.pdf#page=142</p> <p>FAA. (2021, July 26). <em>Order JO 7350.9Z - Location identifiers</em> [Template]. https://www.faa.gov/regulations_policies/orders_notices/index.cfm/go/document.information/documentID/1040529</p> <p>FAA. (2023a). <em>Pilot’s handbook of aeronautical knowledge</em>. https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/phak</p> <p>FAA. (2023b, June 1). <em>Airport Data</em> [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/Airport_Data/</p> <p>FAA. (2024, February 16). <em>Order JO 7400.10F - Special Use Airspace</em> [Template]. https://www.faa.gov/documentLibrary/media/Order/Order_7400.10F_2024_-_final_-signed.pdf</p> <p>ICAO. (2022). <em>North Atlantic (NAT) air navigation plan Volume I (Doc 9634)</em>. https://www.icao.int/EURNAT/EUR%20and%20NAT%20Documents/NAT%20Documents/_eANP%20NAT%20Doc9634/Doc9634%20NAT%20eANP%20Vol%20I.pdf</p> <p>ProAirPilot.com. (2024). <em>Complete list of NOTAM abbreviations</em>. https://proairpilot.com/notam-abbreviations.html</p> <p>SkyVector. (n.d.). <em>Search for Airports by ICAO ID or name</em>. https://skyvector.com/airports</p> <p> </p> <p><strong>Below is the reference to our source of the aircraft traffic and GPS anomalies data, the OpenSky ADS-B database.</strong></p> <p>Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., & Wilhelm, M. (2014). Bringing up OpenSky: A large-scale ADS-B sensor network for research. <em>IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks</em>, 83–94. https://doi.org/10.1109/IPSN.2014.6846743</p> <p> </p>
EVALUATION OF ADVANCED VEHICLE AND COMMUNICATION TECHNOLOGIES THROUGH TRAFFIC MICROSIMULATION
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
US-Accidents: A San Francisco Traffic Accident Dataset
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