Modeling of Passenger Demand using Mixture of Poisson Components

Matej Petrouš, Evženie Suzdaleva, Ivan Nagy

Abstract

The paper deals with the problem of modeling the passenger demand in the tram transportation network. The passenger demand on the individual tram stops is naturally influenced by the number of boarding and disembarking passengers, whose measuring is expensive and therefore they should be modeled and predicted. A mixture of Poisson components with the dynamic pointer estimated by recursive Bayesian estimation algorithms is used to describe the mentioned variables, while their prediction is solved with the help of the Poisson regression. The main contributions of the presented approach are: (i) the model of the number of boarding and disembarking passengers; (ii) the real-time data incorporation into the model; (iii) the recursive estimation algorithm with the normal approximation of the proximity function. The results of experiments with real data and the comparison with theoretical counterparts are demonstrated.

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Paper Citation


in Harvard Style

Petrouš M., Suzdaleva E. and Nagy I. (2019). Modeling of Passenger Demand using Mixture of Poisson Components.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 617-624. DOI: 10.5220/0007831306170624


in Bibtex Style

@conference{icinco19,
author={Matej Petrouš and Evženie Suzdaleva and Ivan Nagy},
title={Modeling of Passenger Demand using Mixture of Poisson Components},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={617-624},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007831306170624},
isbn={978-989-758-380-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Modeling of Passenger Demand using Mixture of Poisson Components
SN - 978-989-758-380-3
AU - Petrouš M.
AU - Suzdaleva E.
AU - Nagy I.
PY - 2019
SP - 617
EP - 624
DO - 10.5220/0007831306170624