Poisson-based framework for predicting count data: Application to traffic counts in Prague areas

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00616467" target="_blank" >RIV/67985556:_____/25:00616467 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/25:00380691

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1877750325000110?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877750325000110?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jocs.2025.102534" target="_blank" >10.1016/j.jocs.2025.102534</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Poisson-based framework for predicting count data: Application to traffic counts in Prague areas

Popis výsledku v původním jazyce

In this paper, we address the task of modeling and predicting count data, with an application to traffic counts on selected urban roads in Prague. We investigated the relationship between multiple counts, designating one of them as the target variable (e.g., data from a key road section) and the others as explanatory counts. Defining traffic count data as the number of vehicles passing through a selected road section per unit of time, we use a framework based on Poisson models to develop a progressive methodology, which we compared with existing models. Working with multimodal count data, we propose the following main steps for the methodology: (i) cluster analysis of explanatory counts using recursive Bayesian estimation of Poisson mixtures. (ii) target count model estimation via local Poisson regressions at identified locations, capturing local relationships between target and explanatory counts. and (iii) prediction of target counts through real-time location detection. The algorithm's properties were first investigated using simulated data and then validated with real traffic counts. Experimental results indicate that the proposed algorithm outperforms classical Poisson and negative binomial regressions, decision tree and random forest classifiers, as well as a multi-layer perceptron, in predicting traffic count data across various quality metrics, even for weakly correlated data. Applied to traffic count data, the promising performance demonstrated by the proposed algorithm offers an optimistic vision for traffic prediction and urban planning, suggesting its potential as a valuable tool for enhancing transportation efficiency by optimizing the timing of city traffic lights to improve traffic flow.

Název v anglickém jazyce

Poisson-based framework for predicting count data: Application to traffic counts in Prague areas

Popis výsledku anglicky

In this paper, we address the task of modeling and predicting count data, with an application to traffic counts on selected urban roads in Prague. We investigated the relationship between multiple counts, designating one of them as the target variable (e.g., data from a key road section) and the others as explanatory counts. Defining traffic count data as the number of vehicles passing through a selected road section per unit of time, we use a framework based on Poisson models to develop a progressive methodology, which we compared with existing models. Working with multimodal count data, we propose the following main steps for the methodology: (i) cluster analysis of explanatory counts using recursive Bayesian estimation of Poisson mixtures. (ii) target count model estimation via local Poisson regressions at identified locations, capturing local relationships between target and explanatory counts. and (iii) prediction of target counts through real-time location detection. The algorithm's properties were first investigated using simulated data and then validated with real traffic counts. Experimental results indicate that the proposed algorithm outperforms classical Poisson and negative binomial regressions, decision tree and random forest classifiers, as well as a multi-layer perceptron, in predicting traffic count data across various quality metrics, even for weakly correlated data. Applied to traffic count data, the promising performance demonstrated by the proposed algorithm offers an optimistic vision for traffic prediction and urban planning, suggesting its potential as a valuable tool for enhancing transportation efficiency by optimizing the timing of city traffic lights to improve traffic flow.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A21009" target="_blank" >8A21009: Embedded storage elements on next MCU generation ready for AI on the edge</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational Science

ISSN

1877-7503

e-ISSN

1877-7511

Svazek periodika

85

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

27

Strana od-do

102534

Kód UT WoS článku

001424860700001

EID výsledku v databázi Scopus

2-s2.0-85217079563