대한교통학회지 (Journal of Korean Society of Transportation)

  • 제14권4호
  • /
  • Pages.49-76
  • /
  • 1996
  • /
  • 1229-1366(pISSN)
  • /
  • 2234-4217(eISSN)
대한교통학회 (Korean Society of Transportation)
권호 표지

복합형GLVQ 신경망을 이용한 차종분류 모형개발

The Development of a Model for Vehicle Type Classification with a Hybrid GLVQ Neural Network

  • 조형기 (아주대학교 교통연구소) ;
  • 오영태 (아주대학교 교통공학과교수)
  • 발행 : 1996.12.01
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초록

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithms, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog Digital Converted data collect form ILD, this study used a typical and modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method, As a result, the hybrid model of GLVQ neural network model is superior to the other methods.

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참고문헌

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