Statistical and Nature-Inspired Modeling of Vehicle Flows by Using Finite Mixtures of Simple Circular Normal Distributions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246442" target="_blank" >RIV/61989100:27240/20:10246442 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216275:25410/20:39916315 RIV/61989100:27740/20:10246442

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9184800" target="_blank" >https://ieeexplore.ieee.org/document/9184800</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MITS.2020.3014419" target="_blank" >10.1109/MITS.2020.3014419</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical and Nature-Inspired Modeling of Vehicle Flows by Using Finite Mixtures of Simple Circular Normal Distributions

Popis výsledku v původním jazyce

The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal (von Mises) statistical distributions. The parameters of the distributions are learned from empirical data by two variants of the expectation-maximization (EM) algorithm and by a nature-inspired method, differential evolution (DE). A proposed statistical model and a fitting strategy are evaluated on real-world data sets describing traffic flows in New York City. The experimental results show that the EM algorithm is able to find model parameters that correspond to input data and that are better than their analytic estimates, while DE evolves even more accurate models. The models based on circular distributions can be represented by circular plots as a novel type of visually appealing and easily interpretable fingerprints of the underlying traffic flow patterns.

Název v anglickém jazyce

Statistical and Nature-Inspired Modeling of Vehicle Flows by Using Finite Mixtures of Simple Circular Normal Distributions

Popis výsledku anglicky

The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal (von Mises) statistical distributions. The parameters of the distributions are learned from empirical data by two variants of the expectation-maximization (EM) algorithm and by a nature-inspired method, differential evolution (DE). A proposed statistical model and a fitting strategy are evaluated on real-world data sets describing traffic flows in New York City. The experimental results show that the EM algorithm is able to find model parameters that correspond to input data and that are better than their analytic estimates, while DE evolves even more accurate models. The models based on circular distributions can be represented by circular plots as a novel type of visually appealing and easily interpretable fingerprints of the underlying traffic flow patterns.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

IEEE Intelligent Transportation Systems Magazine

ISSN

1939-1390

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

182-194

Kód UT WoS článku

000584607700014

EID výsledku v databázi Scopus

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