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  • An effective calibration method of the cellular automaton based traffic microsimulation model is proposed in this paper. It is shown that by utilizing a genetic algorithm it is possible to calibrate different parameters of the model much better than a traffic expert. Moreover, using this process it is also possible to find several model parameters that are extremely difficult to calibrate as relevant data can not be measured using standard monitoring technologies or complete data sets are often not available. The quality of the new calibrated models is discussed in the task of vehicle travel time estimation. The precision of simulations is increased over three times compared to a manually tuned model. The average error rate is 10.75 % in comparison with several field travel time data.
  • An effective calibration method of the cellular automaton based traffic microsimulation model is proposed in this paper. It is shown that by utilizing a genetic algorithm it is possible to calibrate different parameters of the model much better than a traffic expert. Moreover, using this process it is also possible to find several model parameters that are extremely difficult to calibrate as relevant data can not be measured using standard monitoring technologies or complete data sets are often not available. The quality of the new calibrated models is discussed in the task of vehicle travel time estimation. The precision of simulations is increased over three times compared to a manually tuned model. The average error rate is 10.75 % in comparison with several field travel time data. (en)
Title
  • Advanced Approach to Calibration of Traffic Microsimulation Models using Travel Times
  • Advanced Approach to Calibration of Traffic Microsimulation Models using Travel Times (en)
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  • Advanced Approach to Calibration of Traffic Microsimulation Models using Travel Times
  • Advanced Approach to Calibration of Traffic Microsimulation Models using Travel Times (en)
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  • RIV/00216305:26230/13:PU107069!RIV14-TA0-26230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(GAP103/10/1517), P(TE01020155)
http://linked.open...iv/cisloPeriodika
  • 6
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
  • 59604
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/13:PU107069
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • traffic, microsimulation, cellular automaton model, calibration, travel time (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [2BC83B744310]
http://linked.open...i/riv/nazevZdroje
  • Journal of Cellular Automata
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 8
http://linked.open...iv/tvurceVysledku
  • Fučík, Otto
  • Korček, Pavol
  • Sekanina, Lukáš
issn
  • 1557-5969
number of pages
http://localhost/t...ganizacniJednotka
  • 26230
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