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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F10%3A00171806%21RIV11-MSM-21240___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
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 collected GPS 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. 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 collected GPS 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.
dcterms:title
ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION
skos:prefLabel
ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION
skos:notation
RIV/68407700:21240/10:00171806!RIV11-MSM-21240___
n3:aktivita
n6:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n12:2011
n3:domaciTvurceVysledku
n21:5087201
n3:druhVysledku
n17:D
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
285532
n3:idVysledku
RIV/68407700:21240/10:00171806
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Neural Networks; Self-Organizing Maps; Computational Intelligence
n3:klicoveSlovo
n11:Neural%20Networks n11:Computational%20Intelligence n11:Self-Organizing%20Maps
n3:kontrolniKodProRIV
[00D8D4ACBBE1]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Prague
n3:nazevZdroje
Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n12:2010
n3:tvurceVysledku
Skrbek, Miroslav Marek, Rudolf
n3:typAkce
n9:EUR
n3:zahajeniAkce
2010-09-06+02:00
n3:zamer
n13:MSM6840770012
s:numberOfPages
4
n18:hasPublisher
Department of Computer Science and Engineering, FEE, CTU in Prague
n7:isbn
978-80-01-04589-3
n16:organizacniJednotka
21240