The Company Snapshot is a concise electronic record of company identification, size, commodity information, and safety record, including the safety rating (if any), a roadside out-of-service inspection summary, and crash information. The Company Snapshot is available via an ad-hoc query (one carrier at a time) free of charge.

This data were collected during the Safety Pilot Model Deployment (SPMD). The data sets that these entities will provide include basic safety messages (BSM), vehicle trajectories, and various driver-vehicle interaction data, as well as contextual data that describes the circumstances under which the Model Deployment data was collected. Large portion of the data contained in this environment is obtained from on board vehicle devices and roadside units. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Street Team members trained the general public with Vision Zero hands-on safety exercises including safe walking and biking, car safety tips and an opportunity to get inside of large delivery trucks to experience their blind spots. Vision Zero promotional materials were handed out along with educational handouts.

For a complete list of Vision Zero maps, please follow this link

The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6).

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X.
• I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve

Coal burning facilities in the State of Iowa. This data is part of Iowa’s multimodal freight network. These facilities are important for the safe and efficient movement of freight that is demanded by Iowa’s large and diverse economy.

Grain Processing Facilities in Iowa. This data is part of Iowa’s multimodal freight network. These facilities are important for the safe and efficient movement of freight that is demanded by Iowa’s large and diverse economy.

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

Chloride deicers have been applied by the Oregon Department of Transportation (ODOT) to Interstate-5 (I-5) from the Oregon-California border north to mile marker 10 for several years in the high-elevation area known as the Siskiyou Pass. Magnesium chloride (MgCl2) and sodium chloride (NaCl) are applied to keep the interstate safe for drivers and allow for efficient transport of goods and people through adverse weather conditions, particularly snow and ice. The USGS entered into a cooperative agreement with ODOT to research the effects of the application of these chloride deicers in the Carter Creek and Wall Creek watersheds within the Siskiyou Pass. Hydrologic and meteorological data were collected within the study area, and water-quality samples were collected from the Bear Creek watershed, which includes Carter and Wall Creeks. Results indicate a moderate range of natural chloride (Cl), magnesium (Mg) and sodium (Na) concentrations within the Bear Creek watershed, but at Carter and Wall Creeks downstream of I-5, measured constituent concentrations were lower that what was recorded from ODOT’s 2012-2017 pilot project. The Stochastic Empirical Loading and Dilution Model (SELDM) uses a stochastic mass-balance approach to estimate combinations of prestorm streamflow, stormflow, highway runoff, event mean concentrations (EMCs) and loads of stormwater constituents from a site of interest. SELDM was used to evaluate the effects of roadway application of chloride deicers on downstream and highway runoff conditions, particularly EMCs, exceedance rates of hypothetical criteria maximum concentrations (CMCs), and concurrent runoff loads of stormwater constituents from a site of interest. SELDM was also used to evaluate the efficiency of hydrograph extension best-management practices (BMPs) to reduce peak constituent concentrations. In addition, several SELDM scenarios were developed as sensitivity analyses to evaluate the model benefit of collecting specific local sets of data, such as streamflow, precipitation, highway runoff and riverine water-quality samples and volumetric runoff coefficient statistics. These analyses are meant to serve as templates and illustrative examples for ODOT. ODOT is interested in using SELDM for impact analysis and to identify locations and streams that could be vulnerable to excessive deicer loading if chlorides are used. This data release serves as a model archive for the SELDM simulations performed for Stonewall, A.J., Yates, M.C., and Granato, G.E., 2022, Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5091, 94 p., https://doi.org/10.3133/sir20225091. Also included in this data release is a text document that is meant to serve as informal guidance to the Oregon Department of Transportation (ODOT) on how to assess the potential effects of chloride on highway runoff, receiving water and groundwater. This guidance is not meant to be comprehensive nor universal, but rather a compendium of guidance and resources that may help investigators.

Biodiesel plants in the State of Iowa. This data is part of Iowa’s multimodal freight network. These facilities are important for the safe and efficient movement of freight that is demanded by Iowa’s large and diverse economy.

Description:

This dataset contains two parts:
1) 'data' worksheet: measured soil respiration, values of individual measurements across all plots.
2) 'Soil C cycle' worksheet: calculated summaries of the components of the below-ground carbon cycle, including total and component soil respiration (this study), soil carbon pools and flows of organic carbon (previous studies). These data form the basis of the below-ground carbon cycle in Riutta et al 2021 GBC. This sheet contains mean values in each 1 ha carbon plot. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project.

SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/.
Total soil respiration is measured at 25 points per plot, in the middle of each subplot (16 points per plot in OP, in subplot corners), using PVC collars of 10.65 cm internal diameter, inserted into approximately 5 cm depth.
Partitioned respiration is measured at four points per plot, a using a cluster of six collars (see below).
Disturbance experiment in the plot centre to assess the potential bias on fluxes caused by the collar installation.
All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.
The aim is to measure monthly, but in practice the measurement interval is almost always longer (problems with access, staffing and instruments).
EGM-4 infrared CO2 analyser and SRC-1 respiration chamber (PP Systems).
Chamber closure time is 124 seconds, CO2 concentration inside the chamber is recorded every 5 s. Flux is calculated from the linear change in concentration in the chamber headspace.


Conversion from parts per million (ppm) of total gas volume per second per unit collar area to mega grams (1 Mg = 10^6 g) of carbon per hectare per month.

Idea gas law: pV=nRT --> n=pV/(RT)
Mass-Mole: n=m/M --> m=n*M
Combined: m=MpV/(RT)

p (constant) 101,325
R (constant) 8.314472
T temperature in Kelvins --> AirT_Use + 273.15
V headspace volume
M_carbon 12.01

parts per million to absolute units 10^-6
A collar area, m2 0.008825
m2 to hectare 10^4
grams to megagrams 10^-6
seconds to months 2592000

Flux_MgCha-1month-1 = Slope_ppm_s-1 * M* p* V /(R*T) * 10^-6 / A * 10^4 * 10^-6 * 2592000
Soil collar codes Partitioned respiration
C1 All soil respiration components: litter, roots, mycorrhiza, soil organic matter (SOM)
C2 Roots excluded (litter, mycorrhiza, SOM)
C3 Roots and mycorrhiza excluded (litter, SOM)
S1 Litter excluded (roots, mycorrhiza, SOM)
S2 Litter and roots excluded (mycorrhiza, SOM)
S3 Litter, roots and mycorrhiza excluded (only SOM)
D1 Double litter, roots, mycorrhiza, soil organic matter (SOM)
D2 Roots excluded (double litter, mycorrhiza, SOM)
D3 Roots and mycorrhiza excluded (double litter, SOM)
X Organic layer of the soil removed

Disturbance The purpose of the disturbance experiment is to quantify how much disturbance the removal of the roots and mixing the soil causes, compared to just hammering in the deep collar
ND1 Roots severed, not removed and soil not mixed at the installation
ND2 ND1-ND5 are replicates, same treatmet
ND3
ND4
ND5
D1 Roots removed, soil mixed at the installation
D2 D1-D5 are replicates
D3
D4
D5

Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification

Funding: These data were collected as part of research funded by:

• Sime Darby Foundation (Grant, SAFE Core data)
• European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)
• NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)
• NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)
• HSBC Malaysia (Grant)
• The University of Zurich (Grant)

This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following authorities:

• Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This dataset consists of 2 files: SAFE_SoilRespiration_Data_SAFEdatabase_update_2021-01-11.xlsx, SAFE_soil_DATA.zip

SAFE_SoilRespiration_Data_SAFEdatabase_update_2021-01-11.xlsx

This file contains dataset metadata and 2 data tables:

1. Soil respiration data (described in worksheet data)

   Description: Soil respiration data by individual measurements

   Number of fields: 21

   Number of data rows: 20602

   Fields:

   • ForestType: Old-growth, Logged or Oil palm (Field type: categorical)
   • SAFEPlotName: SAFE plot name (Field type: location)
   • PlotName: Plot name (Field type: id)
   • ForestPlotsCode: Plot code in the ForestPlots database (this should be used in publications, instead of plot name). OP plot is not in the ForestPlots database (ForestPlotsCode = NA) (Field type: id)
   • Date: Measurement date (dd/mm/yyyy) (Field type: date)
   • Observers: Observers (Field type: comments)
   • Subplot: Subplot number within each plot, 1-25 (in OP, because the total respiration collars are in subplot corners, no subplot numbers are used, but the collars are refered to as SR1 - SR16. Subplot numbers are used for the partitioned respiration) (Field type: id)
   • MeasurementType: Total, Partitioned or Disturbance (Field type: categorical)
   • CollarType: Total; Partitioned: C1, C2, C3, S1, S2, S3, D1, D2, D3, X; Disturbance: ND1, ND2, ND3, ND4, ND5, D1, D2, D3, D4, D5 (see metadata description for codes) (Field type: id)
   • EGM_RecordNumber: Record number in of the raw flux file. (Field type: id)
   • SoilMoisture: Volumetric soil moisture content (% of pore space) next to the collar. measured with Campbell Scientific Hydrosense sensor with 12 cm rods. (Field type: numeric)
   • SoilT: Soil temperature (°C) is measured with a handheld digital thermometer next to the collar, inserted into 10 cm depth (Field type: numeric)
   • AirT: Air temperature (°C) is measured with a handheld digital thermometer outside the chamber, at the chamber height, in a shaded spot (Field type: numeric)
   • Slope: Slope of the linear regression between time (seconds) from the chamber closure and CO2 concentration (parts per million, ppm) in the chamber headspace. (Field type: numeric)
   • Remarks: Any notes in the field or at data entry stage. 0 = no remarks. If the measurement is repeated in the field multiple times, the other flux estimates are sometimes written in the remarks (not consistent). 2x, 3x etc. indicate multiple repeats. (Field type: comments)
   • CollarHeight: Height from the top of the soil to the top of the collar, mm. This is used for calculating the total headspapce volume (chamber volume + collar volume above the soil surface). (Field type: numeric)
   • HeadspaceVolume: Total headspace volume, sum of the chamber volume (0.001229 m3) and collar volume (d=0.106 m, h=CollarHeight_mm/1000) (Field type: numeric)
   • AirT_Use: Gap filled air temperature data, missing air temperatures replaced with average temperature in logged (27.1), old-growth forest (26.2) and OP (28.7). This is needed for calculating the flux, but should not be used in response functions etc. (Field type: numeric)
   • Flux: Flux corverted from ppm s-1 to Mg carbon per hectare per month. See conversion below. (Field type: numeric)
   • Quality: 1 - good flux; 0 - missing data or bad measurement; 2 - outlier (Field type: numeric)
   • Girdling_0_1: In Tower Plot (SAF-05), subplots 14-25, all trees were girdled in late January - early February 2016. Post-girdling data = 1, if no girdling = 0. (Field type: numeric)

2. Soil carbon cycle (described in worksheet Soil C cycle)

   Description: Estimates of soil carbon pools (fine and coarse root biomass, root and litter necromass, soil organic carbon); fluxes of organic carbon into and respiration out of the different pools. Values are means for each intensive carbon plot.

   Number of fields: 41

   Number of data rows: 11

   Fields:

   • ForestType: Old-growth, Logged or Oil palm (Field type: categorical)
   • SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (Field type: location)
   • PlotName: Plot name (used in field work) (Field type: id)
   • ForestPlotsCode: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)
   • SOC_0to100cm: Soil carbon stock, 0-100 cm layer (Field type:

Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations: An Approach to Improve Energy Recovery and Mitigate Risk: FY1 Final Report The purpose of phase 1 is to determine the feasibility of integrating geologic CO2 storage (GCS) with geothermal energy production. Phase 1 includes reservoir analyses to determine injector/producer well schemes that balance the generation of economically useful flow rates at the producers with the need to manage reservoir overpressure to reduce the risks associated with overpressure, such as induced seismicity and CO2 leakage to overlying aquifers. Based on a range of well schemes, techno-economic analyses of the levelized cost of electricity (LCOE) are conducted to determine the economic benefits of integrating GCS with geothermal energy production. In addition to considering CO2 injection, reservoir analyses are conducted for nitrogen (N2) injection to investigate the potential benefits of incorporating N2 injection with integrated geothermal-GCS, as well as the use of N2 injection as a potential pressure-support and working-fluid option. Phase 1 includes preliminary environmental risk assessments of integrated geothermal-GCS, with the focus on managing reservoir overpressure. Phase 1 also includes an economic survey of pipeline costs, which will be applied in Phase 2 to the analysis of CO2 conveyance costs for techno-economics analyses of integrated geothermal-GCS reservoir sites. Phase 1 also includes a geospatial GIS survey of potential integrated geothermal-GCS reservoir sites, which will be used in Phase 2 to conduct sweet-spot analyses that determine where promising geothermal resources are co-located in sedimentary settings conducive to safe CO2 storage, as well as being in adequate proximity to large stationary CO2 sources.

Motivation

Drowsiness is an intermediate condition that fluctuates between alertness and sleep. It reduces the consciousness level andhinders a person from responding quickly to important road safety issues [1]. The American Automobile Association (AAA) has reported that about 24% of 2,714 drivers that participated in a survey revealed being extremely drowsy while driving, at least once in the last month [2]. In 2017, the National Highway Transportation Safety Administration (NHTSA) also reported 795 fatalities in motor vehicle crashes involving drowsy drivers [3]. Drowsy driving has caused about 2.5% of fatal accidents from 2011 through 2015 in the USA, and it is estimated to produce an economic loss of USD 230 billion annually [4]. Klauer et al. have found in their study that drowsy drivers contributed to 22-24% of crashes or near-crash risks [5]. The German Road Safety Council (DVR) has reported that one out of four fatal highway crashes has been caused by drowsy drivers [6]. In a study carried out in 2015, it has been reported that the average prevalence of falling asleep while driving in the previous two years was about 17% in 19 European countries [6]. The results of these studies emphasize the importance of detecting drowsiness early enough to initiate preventive measures. Drowsiness detection systems are intended to warn the drivers before an upcoming level of drowsiness gets critical to prevent drowsiness-related accidents.

Intelligent Systems that automate motor vehicle driving on the roads are being introduced to the market step-wise. The Society of Automotive Engineers (SAE) issued a standard defining six levels ranging from no driving automation (level 0) to full driving automation (level 5) [7]. While the SAE levels 0-2 require that an attentive driver carries out or at least monitors the dynamic driving task, in the SAE level 3 of automated driving, drivers will be allowed to do a secondary task allowing the system to control the vehicle under limited conditions, e.g., on a motorway. Still, the automation system has to hand back the vehicle guidance to the driver whenever it cannot control the state of the vehicle any more. However, the handover of vehicle control to a drowsy driver is not safe. Therefore, the system should be informed about the state of the driver.

To date, different Advanced Driver Assistance Systems (ADAS) have been made by car manufactures and researchers to improve driving safety and manage the traffic flow. ADAS systems have been benefited from advanced machine perception methods, improved computing hardware systems, and intelligent vehicle control algorithms. By recently increasing the availability of huge amounts of sensor data to ADAS, data-driven approaches are extensively exploited to enhance their performance. The driver drowsiness detection systems have gained much attention from researchers. Before its use in the development of driving automation, drowsiness warning systems have been produced for the direct benefit of avoiding accidents.

The aim of the WACHSens project was to collect a big data set to detect the different levels of driver drowsiness during performing two different driving modes: manual and automated.

To retrieve this data set, please send a request to: arno.eichberger@tugraz.at

References:

[1] M. Awais, N. Badruddin, and M. Drieberg, "A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability,"Sensors, vol. 17, no. 9, 2017, doi: 10.3390/s17091991

[2] AAA Foundation for Traffic Safety, "2019 Traffic Safety Culture Index (Technical Report), June 2020," Washington, D.C., Jun. 2020. [Online]. Available: https://aaafoundation.org/2019-traffic-safety-culture-index/

[3] National Highway Traffic Safety Administration, "Traffic Safety Facts: 2017 Fatal Motor Vehicle Crashes: Overview," NHTSA's National Center for Statistics and Analysis, 1200 New Jersey Avenue SE., Washington DOT HS 812 603, Oct. 2018. Accessed: Apr. 14 2021. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603

[4] Agustina Garcés Correa, Lorena Orosco, and Eric Laciar, "Automatic detection of drowsiness in EEG records based on multimodal analysis," Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, 2014, doi: 10.1016/j.medengphy.2013.07.011

[5] S. Klauer, V. Neale, T. Dingus, Jeremy Sudweeks, and D. J. Ramsey, "The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study," in 2006.

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[7] T. Inagaki and T. B. Sheridan, "A critique of the SAE conditional driving automation definition, and analyses of options for improvement," Cogn Tech Work, vol. 21, no. 4, pp. 569–578, 2019, doi: 10.1007/s10111-018-0471-5

Ethanol plants in the State of Iowa. This data is part of Iowa’s multimodal freight network. These facilities are important for the safe and efficient movement of freight that is demanded by Iowa’s large and diverse economy.

Export Data Access API

NSW Elevation and Depth Theme – Relative Heights

Please Note
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.
In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionality.

Metadata Portal Metadata Information

Hazard Class 1: Class 1 hazards are explosives or any devices or chemicals that are designed to explode or combust. Class 1 explosives are illustrated by an orange placard with their designated hazard class, division number or compatibility letter displayed at the bottom. Some also feature an explosion graphic. There are six different classifications in the explosive class, marked by a sub class number of 1.1 through 1.6, to indicate the type of hazard. Class 1 hazardous materials also have compatibility letters, marked A-S, to help signify what other products are safe to travel with them.

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