Modeling of passenger demand using mixture of Poisson components

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00507178" target="_blank" >RIV/67985556:_____/19:00507178 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/19:00332693

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Modeling of passenger demand using mixture of Poisson components

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modeling of passenger demand using mixture of Poisson components

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A17006" target="_blank" >8A17006: (Ultra)Sound Interfaces and Low Energy iNtegrated SEnsors</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019)

ISBN

978-989-758-380-3

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

617-624

Název nakladatele

SCITEPRESS

Místo vydání

Setubal

Místo konání akce

Prague

Datum konání akce

29. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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