Abstract
This study aims to develop a model that effectively filters outliers in travel time data obtained by using the DSRC on interrupted traffic flow sections. To establish a direction for model development, we identified the characteristics of the DSRC system and existing abnormal data filtering models, causes of outliers in travel time data and their distribution center, changes in the travel time distribution according to the section length and existence of traffic congestion, variation in the number of data samples obtained, and the status of missing data. When an existing model is used to filter the abnormal travel time data obtained on the interrupted traffic flow sections, the mean and median values derived based on the asymmetry of the travel time distribution deviate from the center of the travel time distribution, which leads to fundamental errors in the estimation of the size of the normal travel time data distribution. Moreover, the performance of this model for filtering abnormal data decreases because of the distorted mean and standard deviation when outliers are mixed and optimizing filtering variables is difficult. To overcome these limitations of existing outlier filtering models, we developed a novel outlier filtering model by establishing a model development direction to improve the methods of obtaining data, setting the travel time distribution center, estimating distribution size, and filtering repetitive outliers. The performance of the developed model in filtering outliers is verified by comparing it with that of existing models for the intelligent transport systems installed on 13 types of single-sections and multi-section on the metropolitan national highway, Korea. The comparison indicates that the developed model generally exhibits the best performance for filtering abnormal data on all types of sections. The normal filtering ratio of the developed model was maintained at over 98.5% for all road types and traffic situations, demonstrating improvement of up to 27.4%. The error ratio of travel time at short interrupted traffic flow sections during non-congestion is significantly improved in this model.
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References
Boxel, D. V., Schneider IV, W. H., and Bakula, C. (2011). “An innovative real-time methodology for detecting travel time outliers on interstate highways and urban arterials.” TRB 2011 Annual Meeting CDROM, pp. 60–67, DOI: 10.3141/2256-08.
Clark, S. D., Grant-Muller, S., and Chen, H. (2002). “Cleaning of matched license plate data.” Transportation Research Record No.1804, pp. 1–7, DOI: 10.3141/1804-01
Dion, F. and Rakha, H. (2006). “Estimating dynamic roadway travel times using automatic vehicle identification data.” Transportation Research Part B, Elsevier, pp. 745–766, DOI: 10.1016/j.trb.2005.10.002.
Ferguson, T. S. (1961). “Rules for rejection of outliers, Revue Inst.” Int. de Stat., RINSA, Vol. 29, Issue 3, pp. 29–43, DOI: 10.2307/1401948.
Grubbs, F. (1969). “Procedures for Detecting Outlying Observations in Samples.” Technometrics, Vol. 11, No. 1, pp. 1–21, DOI: 10.1080/00401706.1969.10490657.
Jang, J. H. (2013). “A review on section data cleaning methods.” Transportation Technology and Policy, Vol. 10, No. 2, Korean Society of Transportation, pp. 47–58.
Jang, J. H. and Lim, S. H. (2013). “An outlier filtering algorithm for dedicated short-range communications probe data.” Conference of Korean Society of ITS, Vol. 2013, No.2, Korean Society of ITS, pp. 205–213.
Jung, Y. J., Park, H. S., Kim, B. H., and Kim, Y. C. (2013). “Combined filtering model using voting rule and median absolute deviation for travel time estimation.” Journal of Korean Society of ITS, Vol. 12, No. 6, Korean Society of ITS, pp. 10–21, DOI: 10.12815/kits.2013.12.6.010.
Kang, J. G., Son, Y. T., and Yun, Y. H. (2002). “Regional traffic information acquisition by non-intrusive automatic vehicle identification.” Journal of Korean Society of ITS, Vol. 1, No. 1, Korean Society of ITS, pp. 22–32.
Kendall, M. G. and Stuart, A. (1973). “The Advanced Theory of Statistics.” Vol. 1: Distribution Theory, Edward Arnold, London.
Korea Highway Corporation (2008). “Research on the Development of Practical Technology for Road Traffic Information Detection Systems Using DSRC”.
Korea Institute of Civil Engineering and Building Technology (2009). Report Design Services for the National Highway ITS of Seoul Regional Construction Management Office 2009(2nd), Seoul Regional Construction Management Office.
Korea Institute of Civil Engineering and Building Technology (2014a). National Highway ITS old equipment Improvement Project Final Report 2013, Seoul Regional Construction Management Office.
Korea Institute of Civil Engineering and Building Technology (2014b). National Highway ITS old equipment Improvement Project Final Report 2014, Seoul Regional Construction Management Office.
Lee, H. S. and Nam, K. S. (2009). “A development of preprocessing models of toll collection system data for travel time estimation.” Journal of Korean Society of ITS, Vol. 8, No. 5, Korean Society of ITS, pp. 1–11.
Lim, H. S. (2007). Enhancing the reliability of the link travel time estimation for the interrupted traffic flow, Mokwon University Master’s Thesis.
Ma, X. and Koutsopoulos, H. (2010). “Estimation of the automatic vehicle identification based spatial travel time information collected in stockholm.” IET Intelligent Transport Systems, Vol. 4, Issue 4, pp. 298–306, DOI: 10.1049/iet-its.2009.0149.
Park, H. S. and Kim, Y. C. (2014). “A study on the setting RSE considering the reliability of traffic information.” Conference of Korean Society of ITS, Korean Society of ITS, pp. 257–261.
Park, H. S. and Kim, Y. C. (2017). “Determination of the optimal aggregation interval size of individual vehicle travel times collected by DSRC in interrupted traffic flow section of national highway.” Journal of Korean Society of Transportation, Korean Society of Transportation, pp. 63–78, DOI: 10.7470/jkst.2017.35.1.063.
Seo, S. W. (2002). A Review and Comparision of Methods for Detecting Outliers in Univariate Data Sets, Thesis of Master Degree, Kyunghee University.
Southwest Research Institute (1998). Automatic vehicle identification model deployment initiative-system design document, Report prepared for TransGuide, Texas Department of Transportation.
University of Seoul Industry-Academic Cooperation Foundation (2012). Transportation services Quality Management Plan for the Improvement of Seoul Regional Construction Management Office Road Traffic Information Center Performance, Korea Institute of Civil Engineering and Building Technology.
Yoo, J. S. and Oh, C. S. (1999). Modern Statistics, Park Young Sa.
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Park, H., Kim, Y. Model for Filtering the Outliers in DSRC Travel Time Data on Interrupted Traffic Flow Sections. KSCE J Civ Eng 22, 3607–3619 (2018). https://doi.org/10.1007/s12205-017-1333-z
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DOI: https://doi.org/10.1007/s12205-017-1333-z