Clark, S.D., Watson, S., Redfern, E. and Tight, M.R. (1993) Application of Outlier Detection and Missing Value Estimation Techniques to Various Forms of Traffic Count Data. Working Paper. Institute of Transport Studies, University of Leeds , Leeds, UK.Full text available as:
Available under licence : See the attached licence file.
This paper reports on the application of suitable techniques for detecting outliers and suggesting estimates for missing values in various forrns of traffic count data. The data used in this study came from three sources. The first set was provided by the Department of Transport's (DOT) regional office in Leeds and consists of automatic hourly traffic counts at four sites. The second set was part of a larger database provided by West Yorkshire Highways, Engineering and Technical Services (HETS). This set consists of automatic half hourly traffic counts on a single site. The third and final set was provided by Nottinghan University and consists of automatic five minute traffic counts at 40 locations, in close proximity to each other, from Leicester.
Three suitable techniques emerged from pilot studies of such series conducted by Watson et a1 (1992a) and Redfern et a1 (1992). The three techniques are: a) Maintaining an average and variability measure over time; b) ARIMA modelling with detection of large residuals; C) A point's influence on the correlation structure of the series. A fourth technique, by-eye detection and estimation, provides an intuitive comparison for the first three techniques.
|Item Type:||Monograph (Working Paper)|
|Copyright, Publisher and Additional Information:||Copyright of the Institute of Transport Studies, University Of Leeds|
|Institution:||The University of Leeds|
|Academic Units:||The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)|
|Depositing User:||Adrian May|
|Date Deposited:||18 May 2007|
|Last Modified:||12 Jun 2014 16:26|
|Publisher:||Institute of Transport Studies, University of Leeds|
|Identification Number:||Working Paper 384|
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