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

Result code in 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>

Alternative codes found

RIV/68407700:21260/25:00380691

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

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

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Journal of Computational Science

ISSN

1877-7503

e-ISSN

1877-7511

Volume of the periodical

85

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

27

Pages from-to

102534

UT code for WoS article

001424860700001

EID of the result in the Scopus database

2-s2.0-85217079563