Using Poisson proximity-based weights for traffic flow state prediction

0575836 - ÚTIA 2024 RIV CZ eng J - Journal Article
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
R&D Projects: GA MŠMT 8A21009
Institutional support: RVO:67985556
Keywords : traffic counts * traffic flow state * cluster prediction * Poisson mixture * recursive mixture estimation
OECD category: Statistics and probability
Impact factor: 0.8, year: 2022
Method of publishing: 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.
Permanent Link: https://hdl.handle.net/11104/0345847