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  • 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. (en)
Title
  • ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION
  • ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION (en)
skos:prefLabel
  • ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION
  • ROAD MESH MODELLING USING THE SPATIOTEMPORAL CLUSTERIZATION (en)
skos:notation
  • RIV/68407700:21240/10:00171806!RIV11-MSM-21240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 285532
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21240/10:00171806
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Neural Networks; Self-Organizing Maps; Computational Intelligence (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [00D8D4ACBBE1]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Skrbek, Miroslav
  • Marek, Rudolf
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Department of Computer Science and Engineering, FEE, CTU in Prague
https://schema.org/isbn
  • 978-80-01-04589-3
http://localhost/t...ganizacniJednotka
  • 21240
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