Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops
The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- Yiqiao Li https://orcid.org/0000-0002-2656-9217 © National Academy of Sciences: Transportation Research Board 2021.
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Authors:
- Li, Yiqiao
- 0000-0002-2656-9217
- Tok, Andre
- 0000-0002-0387-0170
- Ritchie, Stephen G
- 0000-0001-7881-0415
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1-16
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2676
- Issue Number: 3
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Axles; Loop detectors; Neural networks; Traffic data; Vehicle classification; Vehicle mix
- Identifier Terms: U.S. Federal Highway Administration
- Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01786444
- Record Type: Publication
- Files: TRIS, TRB, ATRI
- Created Date: Oct 27 2021 4:24PM