In previous research, it has been demonstrated that there is enough variation within the truck population in terms of axle spacings and vehicle lengths, which enable anonymous vehicle re-identification between two measurement stations (e.g., two weigh-in-motion (WIM) sites). Matching trucks between two sites can support various applications, such as calibration of WIM equipment and estimation of travel times and origin-destination flows. In this paper, several modeling approaches to solve the re-identification problem are explored including Naïve Bayes (NB), Bayesian Models (BM) fitted by mixture models, and the formulation of the re-identification problem as a mathematical assignment problem. In addition, the influence of selecting a similarity measure is evaluated through numerical experiments conducted on real-world data from six pairs of upstream–downstream WIM stations. The results demonstrate that solving the re-identification problem with BMs fit by mixture distributions outperforms solving with NB models, while both are outperformed by the mathematical assignment formulation of the same problem, especially when vehicle-pairs exceeding a high threshold of similarity are matched. In addition, expressing the similarity between measurements from two stations as a percentage difference is found to be relatively more advantageous. For the presented pairs of WIM stations, up to 90% matching accuracy can be achieved when the best combination of re-identification method and similarity measure are implemented, and only those vehicle-pairs exceeding a high threshold of similarity are matched.

    Abbreviations AA

    Assignment Algorithm

    BM

    Bayesian Model

    D

    Downstream

    GMM

    Gaussian Mixture Model

    NB

    Naïve Bayes Method

    pdf

    Probability Density Function

    U

    Upstream

    VRI

    Vehicle Re-Identification


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    Title :

    Comparison of vehicle re-identification models for trucks based on axle spacing measurements


    Contributors:

    Published in:

    Publication date :

    2018-11-02


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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