IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Development of Traffic Congestion Prediction Module Using Vehicle Detection System for Intelligent Transportation System

ITS를 위한 차량검지시스템을 기반으로 한 교통 정체 예측 모듈 개발

  • Sin, Won-Sik (Korea Institute Robot Industry Advancement) ;
  • Oh, Se-Do (Department of Industrial and Management Systems Engineering, Kyung Hee University) ;
  • Kim, Young-Jin (Department of Industrial and Management Systems Engineering, Kyung Hee University)
  • 신원식 (한국생산기술연구원 로봇종합지원센터) ;
  • 오세도 (경희대학교 산업경영공학과) ;
  • 김영진 (경희대학교 산업경영공학과)
  • Received : 2010.03.09
  • Accepted : 2010.10.21
  • Published : 2010.12.01
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Abstract

The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

Keywords

References

  1. Chen, M. and Chien, S. (2001), Dynamic freeway travel time prediction using probe vehicle data: Link-based vs. Path-based, Transportation Research Board 80th Annual Meeting, 120-129.
  2. Cheu, R. L., Xie, C., and Lee, D. (2002), Probe Vehicle Population and Sample Size for Arterial Speed Estimation, Computer-Aided Civil and Infrastructure Engineering, 17(1), 53-60. https://doi.org/10.1111/1467-8667.00252
  3. Faouzi, N. E. (2006), Bayesian and Evidential Approaches for Traffic Data Fusion: Methodological Issues and Case Study, Transportation Research Board 85th Annual Meeting, Paper 06-1510 (Preprinted CDROM).
  4. Kwon, J., Coifman, B., and Bickel, P. J. (2000), Day-to-day travel time trends and travel time prediction from loop detector data, Transportation Research Board 79th Annual Meeting, 120-129.
  5. Kim, H-S. (2007), Introduction plan of Archived VDS Data User Services, Proceedings of the KOR-KST Conf. 55th, 449-456.
  6. Rice, J. and Van Zwet, E. (2004), A simple and effective method for predicting travel times on freeways, Intelligent Transport Systems, 5(3), 200-207. https://doi.org/10.1109/TITS.2004.833765