The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Prediction of Speed by Rain Intensity using Road Weather Information System and Vehicle Detection System data

도로기상정보시스템(RWIS)과 차량검지기(VDS) 자료를 이용한 강우수준별 통행속도예측

  • 정은비 (한양대학교 교통공학과) ;
  • 오철 (한양대학교 교통.물류공학과) ;
  • 홍성민 (한양대학교 교통공학과)
  • Received : 2013.04.22
  • Accepted : 2013.06.20
  • Published : 2013.08.31
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Abstract

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

지능형교통체계(ITS: Intelligent Transportation System)의 발전은 과거에 비해 보다 신뢰성 있고 폭넓은 교통자료 및 기상자료 등의 취득을 가능하도록 하였다. 이러한 첨단 시스템의 발전에 따라 수집된 자료를 이용하여 교통상황과 기상상황에 대한 다양한 연구가 활발히 진행되고 있다. 본 연구에서는 도로 기상정보 시스템(RWIS: Road Weather Information System)자료와 검지기 자료를 이용하여 강우량에 따른 속도 감소 패턴을 분석하고, 강우량에 따른 속도감소량 산출 결과를 통해 강우수준을 분류하는 기준을 제시하였다. 인공신경망을 이용하여 강우수준별 통행속도를 예측하였으며, 예측 결과를 비교하여 강우수준별 통행속도 예측 특성을 분석하였다. 분석결과, 강우수준 분류 기준은 0.4mm/5min, 0.8mm/5min으로 나타났으며, 강우수준별 속도와 교통량에 대한 분산분석 결과 강우수준별로 차이를 보이는 것으로 나타났다. 인공신경망을 통한 5분 단위의 통행속도 예측결과, 비강우인 경우에는 과거 5개 자료, 즉, 25분 동안의 속도자료를 사용하여 분석하는 것이 예측력이 높게 나타났으며, 강우가 발생하는 경우에는 과거 2~3개 자료, 즉, 10~15분 동안의 속도자료를 사용하는 것이 예측력이 높게 나타났다. 본 연구에서는 기상조건에 관계없이 신뢰성 있는 교통정보를 제공하기 위한 통행시간 예측 방법론을 제시함으로써 통행시간 정보 등의 교통정보 제공 시 보다 정확한 정보를 제공하여 교통상황 예측정보의 신뢰도 향상 및 교통상황 예측정보의 활용도를 증대시킬 수 있을 것으로 기대된다.

Keywords

References

  1. Y. Al. Hassan, and D. J. Barker, "The impact of unseasonable or extreme weather on traffic activity within Lothian region", Scotland, Journal of Transport Geography, vol. 7, Issue 3, pp.209-213, 1999. https://doi.org/10.1016/S0966-6923(98)00047-7
  2. M. Agarwal, T. H. Maze, and R. Souleyrette, "Impact of weather on urban freeway traffic flow characteristics and facility capacity", Iowa state university, 2005.
  3. S. Datla, and S. Sharma, "Highway Traffic Volume Variations with Cold and Snow Interactions", 2007 Annual Conference and Exhibition of the Transportation Association of Canada: Transportation - An Economic Enabler (Les Transports: Un Levier Economize), pp.21, 2007.
  4. S. Shim, and K. Choi, "Classification of Freeway Traffic Condition by the Impacts of Road Weather Factors", Korean Society of Civil Engineers, Journal of the Korean Society of Civil Engineering, vol. 29, no. 6, pp.685-691, 2009.
  5. C. Yang, Y. Son, Y. Kim, and Y. Kim, "Analysis of Changes in Traffic Flow Patterns by Geometric and Weather Conditions (기하구조 및 기상조건에 따른 교통류 행태 변화에 대한 연구)", Korean Society of Transportation, Transportation Technology and Policy, vol. 6, no. 3, pp.125-140, 2009.
  6. S. Baek, S. Jeong, T. Lee, and J. Won, "Analysis of Snowing Impacts on Freeway Trip Characteristics Using TCS Data", The Korean Institute of Intelligent Transport Systems, The Journal of The Korean Institute of Intelligent Transportation Systems, vol. 9, no. 4, pp.68-79, 2010.
  7. L. Shi, Y. Cheng, J. Jin, B. Ran, and X. Chen, "Effects of Rainfall and Environmental Factors on Traffic Flow Characteristics on Urban Freeway", Transportation Research Board Annual Meeting 2011, Washington D.C., U.S., Paper #11-3345, 2011.
  8. Korea Express Corporation, ROAD PLUS, available: www.roadplus.co.kr.
  9. INRIX, U.S., available: www.inrix.com.
  10. Highway agency, U.K., available: http://www.highways.gov.uk/traffic/traffic.aspx.
  11. G. Huisken, and E. C. van Berkum, "A Comparative Analysis of Short-Range Travel Time Prediction Methods", Transportation Research Board 82nd Annual Meeting, Washington D.C., U.S., 2003.
  12. S. I. Chien, X. Liu, and K. Ozbay, "Predicting Travel Times for The South Jersey Real-Time Motorist Information System", Transportation Research Board 82nd Annual Meeting, Washington D.C., U.S., 2003.
  13. S. Oh, M. Kim, and Y. Baek, "Development of a Freeway Travel Time Estimating and Forecasting Model using Traffic Volume", Korean Society of Transportation, Journal of Korean Society of Transportation, vol. 21, no. 5, pp.83-95, 2003.
  14. S. Lee, B. Kim, and H. Kwon, "The Study of Estimation Model for the Short-term Travel Time Prediction", The Korean Institute of Intelligent Transport Systems, The Journal of The Korean Institute of Intelligent Transportation Systems, vol. 3, no. 1, pp.31-44, 2004.
  15. L. Vanajaskshi, "Travel Time Prediction Using Support Vector Machine Technique", Texas A&M University College Station, 2004.
  16. W. Jang, "A Travel Time Prediction Model under Incidents", Korean Society of Transportation, Journal of Korean Society of Transportation, vol. 29, no. 1, pp.71-79, 2011.
  17. A. F. Hayes, Statistical methods for communication science, Communication books, pp.504, 2011.
  18. Y. Jang, "Origin-Destination Matrix Estimation Method from Partially-Observed Link Counts using Artificial Neural Networks", Chonnam National University, pp.46-50, 2010.
  19. I. Oh, Pattern Recognition, Kyobobook, pp.95-132, 2008.
  20. H. Han, Introduction of pattern recognition, HANBIT media, pp.339-342, 2009.
  21. Ministry of Land, Infrastructure and Transport, Korea Highway Capacity Manual 2013, pp.39, 2013.
  22. R. Hranac, E. Sterzin, D. Krechmer, H. Rakha and M. Farzaneh, "Empirical Studies on Traffic Flow in Inclement Weather", Federal Highway Administration, FHWA-HOP-07-073, 2006.

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