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11 results for “traffic lights”
Brainport, Platooning, platoon with live traffic light
<p><strong>Scenario description</strong>:</p> <p>Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus.<br> - Starting in urban area with speed limits of 15 and 30 km/h.<br> - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights.<br> - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270.<br> - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light.<br> - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus.</p> <p><strong>Session description</strong>:</p> <p>Platoon formation and platooning, with live traffic light data included in planner.<br> - Live traffic light data available for planner<br> - Driver uses the Android app<br> - Starting at default locations<br> - Platooning (CACC and lane keeping) on the A270 when possible.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Platooning, formation improvement by traffic light data
<p><strong>Scenario description</strong>:</p> <p>Platoon formation with live traffic light data included in planner.<br> - Enabled live traffic light data included in planner<br> - Not using the Android app<br> - Starting at default locations<br> - This test was filmed, including the GUI.</p> <p><strong>Session description</strong>:</p> <p>Platoon formation improvement by traffic light data.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
<p><strong>Introduction</strong></p> <p>This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.</p> <p><strong>Changelog</strong></p> <ul> <li><strong>v3.0.0 (2023-03-31):</strong> <ul> <li>Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.</li> </ul> </li> <li><strong>v2.2.1 (2023-03-10):</strong> <ul> <li>A file, <code>location_ratings.csv</code>, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below.</li> <li>The file <code>location.csv</code> has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.</li> </ul> </li> <li><strong>v2.1.0 (2022-12-23)</strong>: <ul> <li>A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated</li> </ul> </li> <li><strong>v2.0.0 (2022-10-17):</strong> <ul> <li>Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building</li> <li>Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects.</li> <li>Added the field <code>plan_id</code> which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan</li> <li>Improved the ordering of fields in the CSV files (instead of alphabetic order)</li> <li>Minor changes to individual sites</li> </ul> </li> </ul> <p><strong>Procurement</strong></p> <p>The data is sourced from commercial clients of <a href="https://www.archilyse.com/">Archilyse AG</a> specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).</p> <p><strong>Geometries</strong></p> <p>The dataset contains a file <code>geometries.csv</code> which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.</p> <p>In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).</p> <p>Each row contains:</p> <ul> <li><code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site</li> <li><code>site_id</code>: The ID of the site</li> <li><code>building_id</code>: The ID of the building</li> <li><code>floor_id</code>: The ID of the floor</li> <li><code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan</li> <li><code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas)</li> <li><code>area_id</code>: The ID of the area in which the element is spatially contained (for features)</li> <li><code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR</li> <li><code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>)</li> <li><code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>)</li> <li><code>geometry</code>: The element’s geometry as a <a href="https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry">WKT</a> geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.</li> <li><code>elevation</code>: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.</li> <li><code>height</code>: The height of the entity in meters, <em>note</em>: In many cases, a default height is assumed</li> </ul> <p>An example:</p> <table> <thead> <tr> <th scope="col">column</th> <th scope="col"> </th> </tr> </thead> <tbody> <tr> <td>apartment_id</td> <td> <p>d4438f2129b30290845ce7eef98a5ba7</p> </td> </tr> <tr> <td>site_id</td> <td>127</td> </tr> <tr> <td>building_id</td> <td>164</td> </tr> <tr> <td>plan_id</td> <td>492</td> </tr> <tr> <td>floor_id</td> <td>861</td> </tr> <tr> <td>unit_id</td> <td>63777</td> </tr> <tr> <td>area_id</td> <td>767676</td> </tr> <tr> <td>unit_usage</td> <td>RESIDENTIAL</td> </tr> <tr> <td>entity_type</td> <td>area</td> </tr> <tr> <td>entity_subtype</td> <td>LIVING_ROOM</td> </tr> <tr> <td>geometry</td> <td> <p>POLYGON ((-6.1501158933490139 -4.8490786654693...</p> </td> </tr> <tr> <td>elevation</td> <td>0</td> </tr> <tr> <td>height</td> <td>2.6</td> </tr> </tbody> </table> <p><strong>Simulations</strong></p> <p>Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as:</p> <pre><code><simulation_category>_<simulation_dimensions>_<aggregation_function></code></pre> <p>For instance. the column <code>view_buildings_median</code> describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.</p> <p><strong>Layout</strong></p> <p>The <em>layout</em> features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.</p> <p>Area Basics / Geometry</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_area_type</td> <td>The area’s area type</td> </tr> <tr> <td>layout_net_area</td> <td>The area’s share of the apartment’s net area (e.g. 0 for a balcony)</td> </tr> <tr> <td>layout_area</td> <td>The area’s actual area</td> </tr> <tr> <td>layout_perimeter</td> <td>The area’s perimeter</td> </tr> <tr> <td>layout_compactness</td> <td>The area’s compactness (the Polsby–Popper score)</td> </tr> <tr> <td>layout_room_count</td> <td>The area’s share to the apartment’s room count</td> </tr> <tr> <td>layout_is_navigable</td> <td>True if the area is navigable by a wheelchair</td> </tr> </tbody> </table> <p>Area Features</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_has_sink</td> <td>True if the area has a sink</td> </tr> <tr> <td>layout_has_shower</td> <td>True if the area has a shower</td> </tr> <tr> <td>layout_has_bathtub</td> <td>True if the area has a bathtub</td> </tr> <tr> <td>layout_has_toilet</td> <td>True if the area has a toilet</td> </tr> <tr> <td>layout_has_stairs</td> <td>True if the area has stairs</td> </tr> <tr> <td>layout_has_entrance_door</td> <td>True if the area is directly leading to an exit of the apartment</td> </tr> </tbody> </table> <p>Area Windows / Doors</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_number_of_doors</td> <td>The number of doors directly leading to the area</td> </tr> <tr> <td>layout_number_of_windows</td> <td>The number of windows of the area</td> </tr> <tr> <td>layout_door_perimeter</td> <td>The sum of all door lengths directly leading to the area</td> </tr> <tr> <td>layout_window_perimeter</td> <td>The sum of all window lengths of the area</td> </tr> </tbody> </table> <p>Area Walls / Railings</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_open_perimeter</td> <td>The sum of all of the boundaries of the area that are neither walls nor railings</td> </tr> <tr> <td>layout_railing_perimeter</td> <td>The sum of all of the boundaries of the area that are railings</td> </tr> <tr> <td>layout_mean_walllengths</td> <td>The mean length of the area’s sides</td> </tr> <tr> <td>layout_std_walllengths</td> <td>The standard deviation of the lengths of the area’s sides</td> </tr> </tbody> </table> <p>Area Adjacency</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_connects_to_bathroom</td> <td>True if the area connects to a bathroom</td> </tr> <tr> <td>layout_connects_to_private_outdoor</td> <td>True if the area connects to an outside area that is private to the apartment</td> </tr> </tbody> </table> <p><strong>View</strong></p> <p>The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> describes the amount of greenery that can be seen from at least 20% of the positions in the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>view_buildings</td> <td>The amount of visible buildings</td> </tr> <tr> <td>view_greenery</td> <td>The amount of visible greenery</td> </tr> <tr> <td>view_ground</td> <td>The amount of visible ground</td> </tr> <tr> <td>view_isovist</td> <td>The amount of visible isovist</td> </tr> <tr> <td>view_mountains_class_2</td> <td>The amount of visible mountains of UN mountain class 2</td> </tr> <tr> <td>view_mountains_class_3</td> <td>The amount of visible mountains of UN mountain class 3</td> </tr> <tr> <td>view_mountains_class_4</td> <td>The amount of visible mountains of UN mountain class 4</td> </tr> <tr> <td>view_mountains_class_5</td> <td>The amount of visible mountains of UN mountain class 5</td> </tr> <tr> <td>view_mountains_class_6</td> <td>The amount of visible mountains of UN mountain class 6</td> </tr> <tr> <td>view_railway_tracks</td> <td>The amount of visible railway_tracks</td> </tr> <tr> <td>view_site</td> <td>The amount of visible site</td> </tr> <tr> <td>view_sky</td> <td>The amount of visible sky</td> </tr> <tr> <td>view_tertiary_streets</td> <td>The amount of visible tertiary_streets</td> </tr> <tr> <td>view_secondary_streets</td> <td>The amount of visible secondary_streets</td> </tr> <tr> <td>view_primary_streets</td> <td>The amount of visible primary_streets</td> </tr> <tr> <td>view_pedestrians</td> <td>The amount of visible pedestrians</td> </tr> <tr> <td>view_highways</td> <td>The amount of visible highways</td> </tr> <tr> <td>view_water</td> <td>The amount of visible water</td> </tr> </tbody> </table> <p><strong>Sun</strong></p> <p>Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> describes the median amount of direct daylight received on the positions in the area.</p> <p>Vernal Equinox</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201803210800</td> <td>Daylight at 08:00 on 21st of March</td> </tr> <tr> <td>sun_201803211000</td> <td>Daylight at 10:00 on 21st of March</td> </tr> <tr> <td>sun_201803211200</td> <td>Daylight at 12:00 on 21st of March</td> </tr> <tr> <td>sun_201803211400</td> <td>Daylight at 14:00 on 21st of March</td> </tr> <tr> <td>sun_201803211600</td> <td>Daylight at 16:00 on 21st of March</td> </tr> <tr> <td>sun_201803211800</td> <td>Daylight at 18:00 on 21st of March</td> </tr> </tbody> </table> <p>Summer Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201806210600</td> <td>Daylight at 06:00 on 21st of June</td> </tr> <tr> <td>sun_201806210800</td> <td>Daylight at 08:00 on 21st of June</td> </tr> <tr> <td>sun_201806211000</td> <td>Daylight at 10:00 on 21st of June</td> </tr> <tr> <td>sun_201806211200</td> <td>Daylight at 12:00 on 21st of June</td> </tr> <tr> <td>sun_201806211400</td> <td>Daylight at 14:00 on 21st of June</td> </tr> <tr> <td>sun_201806211600</td> <td>Daylight at 16:00 on 21st of June</td> </tr> <tr> <td>sun_201806211800</td> <td>Daylight at 18:00 on 21st of June</td> </tr> <tr> <td>sun_201806212000</td> <td>Daylight at 20:00 on 21st of June</td> </tr> </tbody> </table> <p>Winter Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201812211000</td> <td>Daylight at 10:00 on 21st of December</td> </tr> <tr> <td>sun_201812211200</td> <td>Daylight at 12:00 on 21st of December</td> </tr> <tr> <td>sun_201812211400</td> <td>Daylight at 14:00 on 21st of December</td> </tr> <tr> <td>sun_201812211600</td> <td>Daylight at 16:00 on 21st of December</td> </tr> </tbody> </table> <p><strong>Noise / Window Noise</strong></p> <p>Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).</p> <p>Window Noise</p> <p>The noise per window of a given area is aggregated via <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> represents the maximum amount of noise received on any window of the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>window_noise_traffic_day</td> <td>The amount of noise received on the area’s windows from daytime car traffic</td> </tr> <tr> <td>window_noise_traffic_night</td> <td>The amount of noise received on the area’s windows from night-time car traffic</td> </tr> <tr> <td>window_noise_train_day</td> <td>The amount of noise received on the area’s windows from daytime train traffic</td> </tr> <tr> <td>window_noise_train_night</td> <td>The amount of noise received on the area’s windows from night-time train traffic</td> </tr> </tbody> </table> <p>Area-Wise Noise</p> <p>The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, <code>noise_traffic_night</code> describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>noise_traffic_day</td> <td>The amount of noise received in the area from daytime car traffic</td> </tr> <tr> <td>noise_traffic_night</td> <td>The amount of noise received in the area from night-time car traffic</td> </tr> <tr> <td>noise_train_day</td> <td>The amount of noise received in the area from daytime train traffic</td> </tr> <tr> <td>noise_train_night</td> <td>The amount of noise received in the area from night-time train traffic</td> </tr> </tbody> </table> <p><br> <strong>Connectivity</strong></p> <p>Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.</p> <p>The distances and centralities are aggregated via <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> describes the shortest distance to the next balcony from the point closest to the balcony in the area.</p> <p>Distances</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_room_distance</td> <td>Distance to the next area of type ROOM</td> </tr> <tr> <td>connectivity_living_dining_distance</td> <td>Distance to the next area of type LIVING_DINING</td> </tr> <tr> <td>connectivity_bathroom_distance</td> <td>Distance to the next area of type BATHROOM</td> </tr> <tr> <td>connectivity_kitchen_distance</td> <td>Distance to the next area of type KITCHEN</td> </tr> <tr> <td>connectivity_balcony_distance</td> <td>Distance to the next area of type BALCONY</td> </tr> <tr> <td>connectivity_loggia_distance</td> <td>Distance to the next area of type LOGGIA</td> </tr> <tr> <td>connectivity_entrance_door_distance</td> <td>Distance to the next apartment exit</td> </tr> </tbody> </table> <p>Centralities</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_eigen_centrality</td> <td>The Eigen-Centrality value</td> </tr> <tr> <td>connectivity_betweenness_centrality</td> <td>The Betweenness-Centrality value</td> </tr> <tr> <td>connectivity_closeness_centrality</td> <td>The Closeness-Centrality value</td> </tr> </tbody> </table> <p><strong>Location Properties</strong></p> <p>In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file <code>locations.csv</code> contains the simulation data aggregated on a per-building basis. Each row contains the identifier <code>building_id</code> corresponding to the building ids referenced in <code>geometries.csv</code> and <code>simulations.csv</code>.</p> <p><strong>Climate</strong></p> <p>The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from <a href="https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html.">MeteoSwiss</a>. Each column is formatted as <code>climate_<category>_<period>. </code>For instance, the column <code>climate_tnorm_january</code> describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.</p> <p>Temperature Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_tnorm_year</td> <td>The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120)</td> </tr> <tr> <td>climate_tnorm_january</td> <td>The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tnorm_februry</td> <td>The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_tnorm_december</td> <td>The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tminnorm_january</td> <td>The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tminnorm_december</td> <td>The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tmaxnorm_january</td> <td>The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tmaxnorm_december</td> <td>The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> </tbody> </table> <p>Sunshine Duration Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_snorm_year</td> <td>The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m²</td> </tr> <tr> <td>climate_snorm_january</td> <td>The monthly mean relative sunshine duration for January in percent of the current norm period</td> </tr> <tr> <td>climate_snorm_februry</td> <td>The monthly mean relative sunshine duration for February in percent of the current norm period</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_snorm_december</td> <td>The monthly mean relative sunshine duration for December in percent of the current norm period</td> </tr> </tbody> </table> <p>Precipitation Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_rnorm_year</td> <td>The yearly mean precipitation in mm of the current norm period (RnormY9120)</td> </tr> <tr> <td>climate_rnorm_january</td> <td>The monthly mean precipitation for January in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>climate_rnorm_februry</td> <td>The monthly mean precipitation for February in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_rnorm_december</td> <td>The monthly mean precipitation for December mm of the current norm period (RnormM9120)</td> </tr> </tbody> </table> <p><strong>10-Minute Walkshed Infrastructure</strong></p> <p>Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as <code>walkshed_<poi_category>_<poi_type>. </code>For instance, the column <code>walkshed_shop_coffee</code> describes the number of coffee shops located within 10 minutes of walking from the building.</p> <p>The following is an excerpt of support categories and their corresponding types.</p> <ul> <li><code>shop: antique, art, ...</code></li> <li><code>amenity: art, atm, ...</code></li> <li><code>tourism: alpine, attraction, ...</code></li> <li><code>leisure: amusement, beach, ...</code></li> <li><code>healthcare: clinic, dentist, ...</code></li> <li><code>historic: archaeological, battlefield, ...</code></li> <li><code>ariaelway: station</code></li> </ul> <p><strong>Location Ratings</strong></p> <p>The location ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>location_rating_MIKRAT_W</td> <td>Living situation - Overall (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_IMAGE_W</td> <td>Living situation - Image (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_DL_W</td> <td>Living situation - Service Quality (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_FZ_W</td> <td> <p>Living situation - Leisure Quality (1.0=worst, 5.0=best)</p> </td> </tr> <tr> <td>location_rating_NASE_W_DOM</td> <td>The most dominant segment of demand:<br> <br> 1 Rural-traditional<br> 2 Modern worker<br> 3 Transitional-alternative<br> 4 Traditional middle class<br> 5 Liberal middle class<br> 6 Established alternative<br> 7 Upper middle class<br> 8 Professional elite<br> 9 Urban elite<br> 10 Unknown<br> <br> <a href="https://en.fpre.ch/marktdaten/nachfragersegmente/nachfragersegmente-im-wohnungsmarkt/">More Information</a></td> </tr> <tr> <td>location_rating_FGFRQZ</td> <td> <p>The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day.<br> <br> 1 <50<br> 2 50-100<br> 3 100-200<br> 4 200-500<br> 5 >500</p> </td> </tr> </tbody> </table>
CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany
<p>Vehicular ad-Hoc Networks (VANETs) were created to reduce traffic accidents and enhance the safety of both passengers and other road users. In VANETs, vehicles communicate with nearby vehicles using V2V (vehicle-to-vehicle) communication links to improve awareness of their surroundings. Since their inception, considerable research has focused on developing cutting-edge algorithms to boost safety. However, testing these algorithms in real-world conditions is difficult due to high costs and various logistical challenges. Therefore, simulation-based testing is frequently used for VANET applications. Utilizing real datasets in simulations can significantly improve accuracy and yield realistic results. In this study, we introduce a dataset called 'CN+', which comprises data from over 25,000 vehicles collected over 32 hours at a signalized intersection in Bremen, Germany. The dataset is presented in two different formats: 1-CSV format and 2-SUMO converted format.</p>
Behavioral Economics to Implement a Traffic Light Nutrition Ranking System: Study 2
ClinicalTrials.gov study NCT06298253. IPD Sharing: YES. Countries: 1. Publications: 1.
Livorno, Urban driving, Automated vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p> </p> <p><strong>Session description</strong>:</p> <p> </p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, Automated and connected vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</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>
Facts-Up-Front Versus Traffic-Light Food Labels
ClinicalTrials.gov study NCT01626729. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dataset for Traffic Light Classification
<p>This repository contains a dataset with images of traffic lights displaying various colors, intended for training machine learning classification models.</p>
A Trial of Traffic Light Labeling With Behavioral Nudges and a Healthy Recipe Database to Increase Selection of Healthier Foods in Client-choice Food Pantries
ClinicalTrials.gov study NCT04243252. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.