One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation
The focus of this research is to develop a robust model for accurately estimating link-level annual average daily traffic (AADT) of all the local functionally classified roads. The capabilities of one-dimensional convolutional neural network (1D-CNN), a deep learning architecture, and the domain kno...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10315134/ |
| _version_ | 1827742514520522752 |
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| author | Sonu Mathew Srinivas S. Pulugurtha Chaitanya Bhure Sarvani Duvvuri |
| author_facet | Sonu Mathew Srinivas S. Pulugurtha Chaitanya Bhure Sarvani Duvvuri |
| author_sort | Sonu Mathew |
| collection | DOAJ |
| description | The focus of this research is to develop a robust model for accurately estimating link-level annual average daily traffic (AADT) of all the local functionally classified roads. The capabilities of one-dimensional convolutional neural network (1D-CNN), a deep learning architecture, and the domain knowledge pertaining to local road travel characteristics were combined to estimate local road AADT. The AADT based on traffic counts collected at 12,769 traffic count stations on local roads in North Carolina during 2014, 2015, and 2016 were considered for model training, validation, and testing. A total of eight existing state-of-the-art statistical, geospatial, and selected other machine learning models were compared with the 1D-CNN model to estimate local road AADT. These include ordinary least square (OLS) regression, geographically weighted regression (GWR), ordinary kriging, natural neighbor (NN) interpolation, inverse distance weighting (IDW), backpropagation artificial neural network (BP-ANN), random forest (RF), and support vector machine (SVM). The model development and test results showed that the 1D-CNN model performed better than the other considered models. The architecture of the 1D-CNN model can learn the intricate patterns in the local road AADT. The outputs from the methodological framework proposed in this research help practitioners perform safety evaluation, planning and implementing infrastructure improvements, fund allocation and prioritization, air quality estimates, and meeting Highway Safety Improvement Program (HSIP) reporting requirements. |
| first_indexed | 2024-03-11T04:03:00Z |
| format | Article |
| id | doaj.art-c45cabc766af40e1b70a2347553ccfac |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T04:03:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c45cabc766af40e1b70a2347553ccfac2023-11-18T00:01:28ZengIEEEIEEE Access2169-35362023-01-011112722912724110.1109/ACCESS.2023.333212510315134One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic EstimationSonu Mathew0https://orcid.org/0000-0003-2263-2749Srinivas S. Pulugurtha1https://orcid.org/0000-0001-7392-7227Chaitanya Bhure2https://orcid.org/0000-0003-1317-7174Sarvani Duvvuri3Civil and Environmental Engineering Department, The University of North Carolina at Charlotte, Charlotte, NC, USACivil and Environmental Engineering Department, The University of North Carolina at Charlotte, Charlotte, NC, USAElectrical and Computer Engineering Department, The University of North Carolina at Charlotte, Charlotte, NC, USACivil and Environmental Engineering Department, The University of North Carolina at Charlotte, Charlotte, NC, USAThe focus of this research is to develop a robust model for accurately estimating link-level annual average daily traffic (AADT) of all the local functionally classified roads. The capabilities of one-dimensional convolutional neural network (1D-CNN), a deep learning architecture, and the domain knowledge pertaining to local road travel characteristics were combined to estimate local road AADT. The AADT based on traffic counts collected at 12,769 traffic count stations on local roads in North Carolina during 2014, 2015, and 2016 were considered for model training, validation, and testing. A total of eight existing state-of-the-art statistical, geospatial, and selected other machine learning models were compared with the 1D-CNN model to estimate local road AADT. These include ordinary least square (OLS) regression, geographically weighted regression (GWR), ordinary kriging, natural neighbor (NN) interpolation, inverse distance weighting (IDW), backpropagation artificial neural network (BP-ANN), random forest (RF), and support vector machine (SVM). The model development and test results showed that the 1D-CNN model performed better than the other considered models. The architecture of the 1D-CNN model can learn the intricate patterns in the local road AADT. The outputs from the methodological framework proposed in this research help practitioners perform safety evaluation, planning and implementing infrastructure improvements, fund allocation and prioritization, air quality estimates, and meeting Highway Safety Improvement Program (HSIP) reporting requirements.https://ieeexplore.ieee.org/document/10315134/Annual average daily trafficAADTconvolutional neural networksdeep learninglocal roadlow-volume road |
| spellingShingle | Sonu Mathew Srinivas S. Pulugurtha Chaitanya Bhure Sarvani Duvvuri One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation IEEE Access Annual average daily traffic AADT convolutional neural networks deep learning local road low-volume road |
| title | One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation |
| title_full | One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation |
| title_fullStr | One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation |
| title_full_unstemmed | One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation |
| title_short | One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation |
| title_sort | one dimensional convolutional neural network model for local road annual average daily traffic estimation |
| topic | Annual average daily traffic AADT convolutional neural networks deep learning local road low-volume road |
| url | https://ieeexplore.ieee.org/document/10315134/ |
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