Počet záznamů: 1  

Using Poisson proximity-based weights for traffic flow state prediction

  1. 1.
    0575836 - ÚTIA 2024 RIV CZ eng J - Článek v odborném periodiku
    Uglickich, Evženie - Nagy, Ivan
    Using Poisson proximity-based weights for traffic flow state prediction.
    Neural Network World. Roč. 33, č. 4 (2023), s. 291-315. ISSN 1210-0552
    Grant CEP: GA MŠMT 8A21009
    Institucionální podpora: RVO:67985556
    Klíčová slova: traffic counts * traffic flow state * cluster prediction * Poisson mixture * recursive mixture estimation
    Obor OECD: Statistics and probability
    Impakt faktor: 0.8, rok: 2022
    Způsob publikování: Open access
    http://library.utia.cas.cz/separaty/2023/ZS/uglickich-0575836.pdf http://nnw.cz/doi/2023/NNW.2023.33.017.pdf

    The development of traffic state prediction algorithms embedded in intelligent transportation systems is of great importance for improving traffic conditions for drivers and pedestrians. Despite the large number of prediction methods, existing limitations still confirm the need to find a systematic solution and its adaptation to specific traffic data. This paper focuses on the relationship between traffic flow states in different urban locations, where these states are identified as clusters of traffic counts. Extending the recursive Bayesian mixture estimation theory to the Poisson mixtures, the paper uses the mixture pointers to construct the traffic state prediction model. Using the predictive model, the cluster at the target urban location is predicted based on the traffic counts measured in real time at the explanatory urban location. The main contributions of this study are: (i) recursive identification and prediction of the traffic state at each time instant, (ii) straightforward Poisson mixture initialization, and (iii) systematic theoretical background of the prediction approach. Results of testing the prediction algorithm on real traffic counts are presented.
    Trvalý link: https://hdl.handle.net/11104/0345847

     
     
Počet záznamů: 1  

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