Browse > Article

Modelling Missing Traffic Volume Data using Circular Probability Distribution  

Kim, Hyeon-Seok (한국건설기술연구원)
Im, Gang-Won (서울대학교 환경대학원)
Lee, Yeong-In (서울대학교 환경대학원)
Nam, Du-Hui (한성대학교 정보시스템공학과)
Publication Information
Journal of Korean Society of Transportation / v.25, no.4, 2007 , pp. 109-121 More about this Journal
Abstract
In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.
Keywords
Data Missing; Imputation Techniques; Grouping; Circular Probability Distribution; Movm; Mixture Of Von Mises;
Citations & Related Records
연도 인용수 순위
  • Reference
1 FHWA(2001), Traffic Monitoring Guide, FHWA
2 박경수(1992), 신뢰도공학 및 정비이론, 희중당
3 Chandra, C. and Al-Deek, H.(2004), New Algorithms for Filtering and Imputation of Real Time and Archived Dual-Loop Detector Data in the I-4 Data Warehouse, Proc., 83rd TRB Annual Meeting, TRB National Research Council, Washington, D.C
4 Ravindran, P.(2002), Bayesian Analysis of Circular Data using Wrapped Distributions, Ph.D Dissertation of North Carolina State Univ. Raleigh USA
5 Zhong, M., Lingras, P., Sharma, S.C.(2002), Applying Short-term Traffic Prediction Models for Updating Missing Values of Traffic Counts, submitted to the Journal of Transportation Engineering, ASCE
6 오영남(2006), 방향자료에 대한 분석과 모형화, 석사학위논문, 충북대학교
7 장진환, 백남철(2005), 교통량 결측 자료 대체기법 연구, 2005년도 학술발표회 논문집, 대한토목학회
8 Little, R.A., Rubin, D.B.(1987), Statistical Analysis with Missing Data, John Wiley & Sons, New York
9 Ni, D., Leonard, J.D., Guin, A., Feng, C.(2005), Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data, Journal of Transportation Engineering, vol.131, pp.931-938   DOI   ScienceOn
10 Conklin, J.H., Scherer, W.T.(2003), Data Imputation Strategies for Transportation Management Systems, Research Project Report, Center for Transportation Studies, Univ. of Virginia
11 Pigott, T.D.(2001), A Review of Methods for Missing Data, Educational Research and Evaluation vol.4 pp.353-383
12 Gold, D.L., Turner, S.M., Gajewski, B.J., Spiegelman, C.(2001), Imputing Missing Values in ITS Data Archives for Intervals under 5 Minutes, Proc., 80th Transportation Research Board(TRB) Annual Meeting, TRB National Research Council, Washington, D.C
13 Sharma, S.C., Lingras, P., Zhong, M.(2003), Effect of Missing Value Imputations on Traffic Parameters Estimations from Permanent Traffic Counts, Proc., 82nd Transportation Research Board(TRB) Annual Meeting, TRB National Research Council, Washington, D.C