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8 results for “road condition”
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
Geospatial Analysis of Road Conditions and Hazardous Factors in Communities on Continuous vs. Sporadic Permafrost in Greenland
<p>Road conditions and hazardous factors were surveyed in two permafrost-affected communities of West Greenland, Ilulissat (underlain by continuous ice-rich permafrost) and Sisimiut (underlain by sporadic permafrost). Pavement damages, repairs, embankment structural elements, artificial drainage systems, water accumulations and preferential snow ploughing deposits were notably mapped and georeferenced in a geographic information system to form high-resolution spatial databases. In total, respectively 66 and 76 \% of the paved road networks of Ilulissat and Sisimiut were surveyed. Manual in-situ mapping took place in September 2020 and September 2021 in Ilulissat, while Global Navigation Satellite System (GNSS) equipment was used to map road conditions in Sisimiut in September 2020. The severity of the pavement damages was assessed according to the ASTM D 6433–07, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, by ASTM International (2008). The drainage conditions were characterized following the recommendations in Cold Regions Pavement Engineering, by Doré, G. and Zubeck, H. K. (2009).</p> <p>This dataset comprises the geospatial layers of the road damage and hazard inventories created for the settlements of Ilulissat and Sisimiut. Each settlement’s inventory is provided in a ZIP-folder, containing the geospatial layers as geopackages and sorted following a thematic structure. Further information about each layer and its attributes can be found in the metadata PDF document.</p>
Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade
<p>Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO<sub>2</sub> emissions in this study. CO<sub>2</sub> model results using VSP computed with the measured real-world road grade (R<sup>2</sup> = 0.86) gave improved fits over the no-grade VSP model (R<sup>2</sup> = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.</p>
Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade
Open the record for dataset details and reuse information.
Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters, Data
<p>Data for Paper "Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters"</p>
Road condition dataset from LTPP database
<p>The dataset is collected from the LTPP database.</p>
Road Condition Image Dataset
<p>Datasets containing 2D images from roads. The images were either rendered from 3D lidar point clouds or captured by a camera on a so called Mobile Mapping vehicle. For more information on how to use this dataset, visit the following repository:</p> <p>https://github.com/Snagnar/CompetitiveReconstructionNetworks</p>
Sweat Rate Measurement With a Sensor (Wearable) in Different Climatic Conditions With Road Cyclists.
ClinicalTrials.gov study NCT06308432. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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