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  • The traffic density map (TDM) represents the density of road network traffic as the number of vehicles per a specific time interval. TDMs are used by traffic experts as a base documentation for planning a new infrastructure (long-term) or by drivers for showing a current trafic status (short-term). We propose two methods for estimation of missing density values in TDMs. In the first method, the problem is formulated relatively strictly in terms of quadratic programming (QP) and a QP solver is utilized to find a solution. The second, more general method is based on a multiobjective genetic algorithm which allows us to find a reasonable compromise among several objectives that a traffic expert may formulate. These two methods can work automatically or they can be used by a traffic expert for an iterative density estimation. Results of experimental evaluation based on real and randomly generated data are presented.
  • The traffic density map (TDM) represents the density of road network traffic as the number of vehicles per a specific time interval. TDMs are used by traffic experts as a base documentation for planning a new infrastructure (long-term) or by drivers for showing a current trafic status (short-term). We propose two methods for estimation of missing density values in TDMs. In the first method, the problem is formulated relatively strictly in terms of quadratic programming (QP) and a QP solver is utilized to find a solution. The second, more general method is based on a multiobjective genetic algorithm which allows us to find a reasonable compromise among several objectives that a traffic expert may formulate. These two methods can work automatically or they can be used by a traffic expert for an iterative density estimation. Results of experimental evaluation based on real and randomly generated data are presented. (en)
Title
  • Estimation of traffic density map using evolutionary algorithm
  • Estimation of traffic density map using evolutionary algorithm (en)
skos:prefLabel
  • Estimation of traffic density map using evolutionary algorithm
  • Estimation of traffic density map using evolutionary algorithm (en)
skos:notation
  • RIV/00216305:26230/12:PU98026!RIV13-GA0-26230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • P(ED1.1.00/02.0070), P(GAP103/10/1517), S, Z(MSM0021630528)
http://linked.open...vai/riv/dodaniDat
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  • 134812
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  • RIV/00216305:26230/12:PU98026
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  • Optimization and Control: Theory and Modeling,Statistical Modeling, Data Mining and Analysis (en)
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  • [4237AB02F901]
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  • Anchorage
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  • Anchorage
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  • Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems
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  • Fučík, Otto
  • Korček, Pavol
  • Sekanina, Lukáš
  • Petrlík, Jiří
  • Beszédeš, Marián
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000312599600105
http://linked.open.../riv/zahajeniAkce
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number of pages
http://bibframe.org/vocab/doi
  • 10.1109/ITSC.2012.6338757
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  • IEEE Intelligent Transportation Systems Society
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  • 978-1-4673-3062-6
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  • 26230
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