DOI: 10.21949/1531020
title: Data Fusion to Improve the Accuracy of Traffic Counts [supporting dataset]

description: Dataset Location: The main dataset page is a GitHub README file with 2 links (https://github.com/reek129/C2m2). The dataset is currently stored on the Internet Archive. The item "c2m2_qeloise_data" is currently too large to be zipped by the Internet Archive. To access this item, please contact the Internet Archive team (info@archive.org).



GitHub: https://github.com/reek129/C2m2

c2m2_qeloise_data: https://archive.org/details/c2m2_qeloise_data

c2m2_qeloise_thermal_data_combined: https://archive.org/details/c2m2-qeloise-thermal-data-combined



National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.8083359). NTL staff last accessed this dataset at its repository URL on 2024-0-30. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
description: This study investigated the use of data fusion of two different traffic counting and classification methods. While sensor level fusion using thermal and optical images was investigated, it was not found useful by our approaches. The decision-level fusion method for pneumatic tube and infrared video is presented. The method was validated at three different locations in South Carolina. Errors in vehicle counts and vehicle classification were calculated using manual data collection from recorded videos as the baseline. In all locations, the results of data fusion are more accurate in both vehicle counts and vehicle classification when compared to either of the methods alone.



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