Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783638 | PMC |
| http://dx.doi.org/10.3390/s22249876 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Impact of time from blood specimens collection to centrifugation on the diagnosis of gestational diabetes mellitus.
Ann Med
December 2025
Department of Laboratory Medicine, The Women's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Objective: The process of glycolysis from blood collection to centrifugation impacts the diagnosis of gestational diabetes mellitus (GDM). However, the specific characteristics of the working environment in China and its influence on GDM diagnosis still need to be clarified.
Methods: Firstly, 15 pregnant women were recruited, and six specimens were collected from each in a fasting state.
The missing piece: The clinical translation of precision diabetes medicine requires precision mental health care: A call to action from the international PsychoSocial Aspects of Diabetes (PSAD) Study Group.
Diabet Med
January 2025
Department of Psychology, University of Southern Denmark, Odense, Denmark.
Compressive electron backscatter diffraction imaging.
J Microsc
January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFA Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting.
Clin Chem
January 2025
Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
View Article and Find Full Text PDFDeep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research.
Sensors (Basel)
January 2025
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!