Visual analysis of vehicle trajectories for determining cross-sectional load density

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44994575%3A_____%2F19%3AN0000090" target="_blank" >RIV/44994575:_____/19:N0000090 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26230/19:PU134162

Výsledek na webu

<a href="https://tots.upol.cz/artkey/tot-201901-0006_visual-analysis-of-vehicle-trajectories-for-determining-cross-sectional-load-density.php" target="_blank" >https://tots.upol.cz/artkey/tot-201901-0006_visual-analysis-of-vehicle-trajectories-for-determining-cross-sectional-load-density.php</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5507/tots.2019.002" target="_blank" >10.5507/tots.2019.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Visual analysis of vehicle trajectories for determining cross-sectional load density

Popis výsledku v původním jazyce

The goal of this work was to analyze the behavior of drivers on third class roads with and without horizontal lane marking. The roads have low traffic volume, and therefore a conventional short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles by means of computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be difficult to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curve.

Název v anglickém jazyce

Visual analysis of vehicle trajectories for determining cross-sectional load density

Popis výsledku anglicky

The goal of this work was to analyze the behavior of drivers on third class roads with and without horizontal lane marking. The roads have low traffic volume, and therefore a conventional short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles by means of computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be difficult to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curve.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20104 - Transport engineering

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)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Transactions on Transport Sciences

ISSN

1802-9876

e-ISSN

—

Svazek periodika

2019

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

50-57

Kód UT WoS článku

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EID výsledku v databázi Scopus

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