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A Study on Estimation of Vehicle Miles Traveled

자동차주행거리 추정방안 연구

  • 안원철 ((주)베가스 컨설팅사업본부) ;
  • 박동주 (서울시립대학교 교통공학과) ;
  • 허태영 (충북대학교 정보통계학과) ;
  • 연지윤 (한국교통연구원 국가교통DB센터) ;
  • 김찬성 (한국교통연구원 국가교통DB센터)
  • Received : 2014.09.24
  • Accepted : 2014.11.22
  • Published : 2014.12.31

Abstract

This study identified the causes of errors that could take place in the estimation process of vehicle miles traveled and quantified the effects of each of those causes on the estimation accuracy of vehicle miles traveled via error rate to propose an efficient way to estimate vehicle miles traveled. The study proceeded as follows: first, the study established survey data of vehicle miles traveled in the pilot test areas to test the accuracy of a method to estimate vehicle miles traveled. Second, the causes of errors with the estimation of vehicle miles traveled were categorized into errors with the sample size, sampling methods, and homogeneous link setting methods. In addition, many different methodologies were set to minimize errors with the estimation of vehicle miles traveled according to each of the causes. Third, error rates of estimation of vehicle miles traveled were compared and analyzed according to each of the methodologies. Finally, a toy network was established to propose a way of estimating vehicle miles traveled by taking the local characteristics into consideration. The study finds its significance in that it proposed an efficient way to estimate vehicle miles traveled through an experiment and planning approach and made use of survey data of vehicle miles traveled to test estimation accuracy. The proposed way of estimating vehicle miles traveled by taking into account the local characteristics will make a contribution to the estimation of vehicle miles traveled by the areas in future along with the level of data offered in the study.

본 연구는 자동차주행거리 추정과정에서 발생할 수 있는 오차발생 원인을 규명하였다. 그리고 각 원인이 자동차주행거리 추정 정확도에 미치는 영향을 오차율로 정량화하여 효율적인 자동차주행거리 추정방안을 제시하였다. 이를 위한 연구과정은 다음과 같다. 첫째, 시범조사 지역을 대상으로 자동차주행거리 추정 방법론의 정확도를 검증하기 위한 자동차주행거리 관측 자료를 구축하였다. 둘째, 자동차주행거리 추정 오차발생 원인은 표본크기, 표본추출방법, 단위구간 설정방법의 오류로 구분하였다. 그리고 각 원인에 따른 자동차주행거리 추정오차를 최소화하기 위한 다양한 방법론을 설정하였다. 셋째, 각 방법론에 의한 자동차주행거리 추정 오차율을 비교분석 하였다. 마지막으로 Toy-Network를 구축하여 지역특성을 고려한 자동차주행거리 추정방안을 제시하였다. 본 연구는 실험 계획적 접근방법을 통하여 효율적인 자동차주행거리 추정방안을 제시하였으며, 추정 정확도 검증을 위하여 자동차주행거리 관측 자료를 활용했다는 점에서 의의를 갖는다. 또한 본 연구에서 제시한 자료수준과 지역특성을 고려한 자동차주행거리 추정 방안은 향후 지역별 자동차주행거리 추정에 기여할 것으로 판단된다.

Keywords

References

  1. Kumapley, R.K. & J.D. Fricker, "Review of Methods for Estimating Vehicle Miles Traveled", Transportation Research Record, no. 1551, pp.59-66. 1996.
  2. Gadda, S., K. Kockelman, A. Maggon, "Estimates of AADT: Quantifying the Uncertainty", Presented at the June 2007 World Conference on Transportation Research, Berkeley, California, May 20. 2007.
  3. FHWA, Federal Highway Administration: Highway Performance Monitoring System Field Manual, Washington, D.C. 2012.
  4. Do, M. S., Kim, S. H., Moon, H. Y., Kim, M. S., "Classification Method of Homogeneous Road Section for National Highway", Korean Society of Civil Engineering, vol. 24, no. 4, pp.523-533. 2004.
  5. Frawley, W.E., "Random Count Site Selection Process for Statistically Valid Estimations of Local Street Vehicle Miles Traveled", Transportation Research Record, no. 1993, pp.43-50. 2007.
  6. Mohamad, D., K.C. Sinha, T. Kuczek & C.F. Scholer., "Annual Average Daily Traffic Prediction Model for County Roads", Transportation Research Record, no. 1617, pp.69-77. 1998.
  7. Xia, Q., F. Zhao, Z. Chen, L. D. Shen, & D. Ospina, "Estimation of Annual Average Daily Traffic for Nonstate Roads in a Florida County", Transportation Research Record, No.1660, pp.32-40. 1999.
  8. Zhao, F. & S. Chung. "Contributing Factors of Annual Average Daily Traffic in a Florida County: Exploration with Geographic Information System and Regression Models", Transportation Research Record, no. 1769, pp.113-122. 2001.
  9. Lam, W.H.K. & J. Xu, "Estimation of AADT from Short Period Counts in Hong Kong: A Comparison Between Neural Network Method and Regression Analysis", Journal of Advanced Transportation, vol. 34, pp.249-268. 2000. https://doi.org/10.1002/atr.5670340205
  10. Sharma, S.C., B.M. Gulati & S. Rizak, "Statewide Traffic Volume Studies and Precision of AADT Estimates", Journal of Transportation Engineering, vol. 122, pp.430-439. 1996. https://doi.org/10.1061/(ASCE)0733-947X(1996)122:6(430)
  11. Davis, G. & S. Yang., "Accounting for Uncertainty in Estimates of Total Traffic Volume: An Empirical Bayes Approach", Journal of Transportation and Statistics, vol. 4, no. 1, pp.27-38. 2001.
  12. Selby, B. & K., Kockelman, "Spatial Prediction Of AADT In Unmeasured Locations By Universal Kriging", 90th Annual Meeting of the Transportation Research Board, Washington, D.C., DVD-ROM, 2011.
  13. Wang, X. & K. Kockelman, "Forecasting Network Data: Spatial Interpolation of Traffic Counts Using Texas Data", Transportation Research Record, no. 2105, pp.100-108. 2009.
  14. Eom, J.K., M.S. Park, T.Y. Heo, L.F. Huntsinger, "Improving the Prediction of Annual Average Daily Traffic for Non-freeway Facilities by Applying a Spatial Statistical Method", Transportation Research Record, no. 1968, pp.20-29. 2006.
  15. FHWA, Federal Highway Administration: Traffic Monitoring Guide, Washington, D.C. 2001.
  16. Fisher, R. A., "On a distribution yielding the error functions of several well known statistics", Proceedings of the International Mathematical Congress, Toronto, Canada, August 11-16. 1924.
  17. Guttman, L., An Outline of the Statistical Theory of Prediction, In: P. Horst (Ed.) The Prediction of Personal Adjustment, New York: Social Science Research Council, 1941.