ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION

Kód výsledku v IS VaVaI

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RIV/68407700:21240/10:00171806

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION

Popis výsledku v původním jazyce

The GPS navigation is widely used aid for travelers. However, good navigation depends on good maps, which are sometimes hard to get. In this paper we explore a method to model a road mesh using self-organizing spatiotemporal data clustering of collectedGPS data. The resulting road mesh model is obtained from simulated self organizing neural network, which clusters the individual data vectors and infers the time dependencies between the clusters. This allows to detect one way roads, or slow traffic automatically. To achive this goal we model the road mesh with the Temporal Hebbian Self-organizing map (THSOM). This paper presents a novel training method for the simulated THSOM neural network, which reduces training period and improves model the convergence of original THSOM neural network. The road mesh model is obtained from real GPS data as well as from simulated data set.

Název v anglickém jazyce

ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION

Popis výsledku anglicky

The GPS navigation is widely used aid for travelers. However, good navigation depends on good maps, which are sometimes hard to get. In this paper we explore a method to model a road mesh using self-organizing spatiotemporal data clustering of collectedGPS data. The resulting road mesh model is obtained from simulated self organizing neural network, which clusters the individual data vectors and infers the time dependencies between the clusters. This allows to detect one way roads, or slow traffic automatically. To achive this goal we model the road mesh with the Temporal Hebbian Self-organizing map (THSOM). This paper presents a novel training method for the simulated THSOM neural network, which reduces training period and improves model the convergence of original THSOM neural network. The road mesh model is obtained from real GPS data as well as from simulated data set.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers

ISBN

978-80-01-04589-3

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

Department of Computer Science and Engineering, FEE, CTU in Prague

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

6. 9. 2010

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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