United States Department of Transportation
i
Influence of Pavement Conditions on Commercial Motor Vehicle Crashes [supporting dataset]
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2023-12-01
[JSON-3.59 KB]
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Abstract:Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary logistic regression to explore relationships between injury occurrence after a CMV crash and multiple independent variables. Additionally, Artificial Neural Network (ANN) models were developed to classify CMV crash injury severity. Importantly, the inclusion of pavement condition variables (International Roughness Index and Pavement Condition Index) substantially enhanced the accuracy of the logistic regression model, increasing predictability from 0.8% to 41%. The study also demonstrates the potential of Artificial Neural Network models in predicting CMV crash injury severity, achieving an accuracy of 60% and an F-measure of 0.52. These results highlight the importance of considering road pavement conditions in road safety policies and interventions. The study provides valuable insights for policymakers and stakeholders aiming to enhance road safety for CMVs in the District of Columbia and showcases the potential of machine learning techniques in understanding the complex interplay between road conditions and CMV crash occurrences.
The total size of the zip file is 137 KB. The .xlsx and .xls file types are Microsoft Excel files, which can be opened with Excel, and other free available spreadsheet software, such as OpenRefine. The .txt file type is a common text file, which can be opened with a basic text editor. The most common software used to open .txt files are Microsoft Windows Notepad, Sublime Text, Atom, and TextEdit (for more information on .txt files and software, please visit https://www.file-extensions.org/txt-file-extension).
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Content Notes: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. The current level of dataset documentation is the responsibility of the dataset creator. 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 2024-01-29. 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.
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