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.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-10-10




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Federal Highway Administration Vehicle Classification from Video Data and a Disaggregation Model

    Graettinger, A. J. / Kilim, R. R. / Govindu, M. R. et al. | British Library Online Contents | 2005



    Federal Highway Administration Innovative Financing

    Inman, M. | British Library Conference Proceedings | 1998


    The Federal Highway Administration Perspective

    Mathis, D. / National Research Council (U.S.) | British Library Conference Proceedings | 2008


    The Intelligent Vehicle Highway Systems: The Federal Highway Administration R&D Program

    Santiago, A. J. / American Association for Artificial Intelligence | British Library Conference Proceedings | 1994