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

  • 제12권6호
  • /
  • Pages.10-21
  • /
  • 2013
  • /
  • 1738-0774(pISSN)
  • /
  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
권호 표지
DOI QR코드

DOI QR Code

통행시간 추정을 위한 Voting Rule과 중위절대편차법 기반의 복합 필터링 모형

Combined Filtering Model Using Voting Rule and Median Absolute Deviation for Travel Time Estimation

  • 투고 : 2013.10.21
  • 심사 : 2013.11.22
  • 발행 : 2013.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 교통정보시스템에서 통행시간의 이상치 자료를 제거하기 위한 복합 필터링 모형을 제시하였으며, 이는 중위절대편차법과 Voting Rule을 기반으로 하는 이중화된 필터링 모형에 해당한다. 본 모형은 중위절대편차법을 이용해 표본을 정규분포화 시키기 위한 1차 필터링을 수행하며, 이후 Voting Rule을 이용해 중위절대편차법의 적용 이후에도 남아 있는 이상치 자료를 제거하는 방식에 해당한다. 이때 Voting Rule은 표본의 통행시간과 평균통행시간의 차이가 임계치를 초과하는 경우 해당 표본을 이상치로 판정하며, 다수결의 원칙을 이용하여 이상치 자료의 비율에 따라 이상치에 대한 제거 여부를 결정한다. 일반국도 3호선의 경기도 광주시 구간을 대상으로 한 사례분석을 통해 복합 필터링 모형이 이상치 표본 만을 선택적으로 제거하여 통행시간 추정의 정확도 개선에 기여할 수 있음을 확인하였다.

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

키워드

참고문헌

  1. S. H. Lim, "Situation of Planning and Prospects of National Highway ITS", Transportation Technology and Policy, vol. 26, no. 5, pp.61-68, 2008.
  2. University of Seoul, Transportation Information Quality Assurance for Performance Improvement of Transportation Information Center, pp.1-2, 2012.
  3. Korea Institute of Construction Technology, 2012 Work Manual for Operation and Management of Transportation Information Center, pp.155, 2012.
  4. V. Barnett and T. Lewis, Outliers in Statistical Data, John Wiley & Sons, 1985.
  5. B. Iglewicz and D. Hoaglin, How to detect and handle outliers, ASQC Quality Press, 1993.
  6. S. D. Clark, S. Grant-Muller, and H. Chen, "Cleaning of Matched License Plate Data", Transportation Research Record 1804, pp.1-7, 2002.
  7. J. K. Kang, Y. T. Son, Y. H. Yoon, and S. C. Byun, "Regional Traffic Information Acquisition by Non-intrusive Automatic Vehicle Identification", Journal of Korea ITS Society, vol. 1, no. 1, pp.22-32, 2002.
  8. J. M. Roth, A Time Series Approach To Removing Outlying Data Points From Bluetooth Vehicle Speed Data, Thesis of Master Degree, The University of Akron, 2010.
  9. C. Bilen and S. Huzurbazar, "Wavelet-Based detection of outliers in time series", Journal of Computational and Graphical Statistics, vol 21, pp.311-327, 2012.
  10. K. J. Choi and J. A. Jang, "A Time Series-based Algorithm for Eliminating Outliers of GPS Probe Data", Journal of Korea Society of Transportation, vol. 22, no. 6, pp.67-75, 2004
  11. S. W. Seo, A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets, Thesis of Master Degree, Kyung Hee University, 2002.
  12. F. Grubbs, "Procedures for Detecting Outlying Observations in Samples", Technometrics, vol. 11, no. 1, pp.1-21, 1969. https://doi.org/10.1080/00401706.1969.10490657
  13. M. S. Ross, "Peirce's criterion for the elimination of suspect experimental data", Journal of Engineering Technology, vol. 20, no. 2, pp.1-12, 2003.
  14. F. Dion and H. Rakha, "Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates", Transportation Research Part B, vol. 40, no. 9, pp.745-766, 2006. https://doi.org/10.1016/j.trb.2005.10.002
  15. K. K. Srinivasan and P. P. Jovanis, "Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network", Transportation Research Record 1537, pp.15-22, 1996.
  16. Y. I. Lee and J. H. Lee, "A Study on Link Travel Time Estimating Methodology for Traffic Information Service (Determination of an Adequate Sample Size)", Journal of Korea Society of Transportation, vol. 20, no. 3, pp.55-67, 2002.
  17. M. Lines, "Approval Voting and Strategy Analysis: A Venetian Example", Theory and Decision, vol. 20, pp.155-172, 1986. https://doi.org/10.1007/BF00135090
  18. H. Nurmi, Comparing Voting Systems, D. Reidel, Dordrecht, Holland, 1987.

피인용 문헌

  1. Design and Implementation of IoT-Based Intelligent Platform for Water Level Monitoring vol.21, pp.4, 2015, https://doi.org/10.7851/ksrp.2015.21.4.177
  2. Compact UWB Band-pass Filter with Open-Stub Using Impedance Mismatching and SIR vol.51, pp.6, 2014, https://doi.org/10.5573/ieie.2014.51.6.282
  3. 프로브 자료의 특성을 고려한 단속류의 구간 통행속도 산출에 관한 연구 vol.34, pp.6, 2014, https://doi.org/10.12652/ksce.2014.34.6.1851
  4. 밀도기반 비집계 접근법을 이용한 구간통행시간 추정 방법론 vol.16, pp.5, 2013, https://doi.org/10.12815/kits.2017.16.5.134
  5. 드라이빙 시뮬레이터를 이용한 자율주행자동차 제어권 전환 소요시간 및 안정화 특성 분석 vol.18, pp.4, 2019, https://doi.org/10.12815/kits.2019.18.4.31