AP-G84/04 Best practice in road use data collection, analysis ... - WIM

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Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005 Table 17 – RTA NSW rejection rules on classified counts Daily reject criterion ≤ 5 veh / day or Hourly reject criterion 0 veh for more than 12 hours Austroads 2004 — 42 — Best Practices in Road Use Data Collection, Analysis and Reporting > 14% Class 3 (two-axle truck or bus with wheelbase more than 3.2 m) or > 20% Class 13 (error bin, with less than 1,000 error vehicles per day) > 20% Class 13 (error bin with less than 100 error vehicles per hour) Main Roads WA adopts a definition of AADT slightly different from GTEP Part 3. AADT is determined using the Monthly Average Day of the Week Traffic (MADW) and Annual Average Days of the Week (AADW) as follows: MADW ij = V n w ij ij ∑ w= 1 nij 12 ∑ j= 1 (84 values in a year) 1 AADW i = MADWij (7 values in a year) 12 1 AADT = 7 7 ∑ AADWi i= 1 where i is the weekday index with i = 1 to 7 (Monday to Sunday), j is the monthly index with j = 1 to 12 (January to December), w is the week index with w = 1 to nij = 1 to 5, nij is the number of a weekday in a month; (e.g. one to five Mondays in a month), w V ij is the traffic volume on ith weekday in the wth week in the jth month, MADW is the monthly average traffic for each day of the week, over the period of one month, and AADW is the annual average traffic for each day of the week, over the period of one year. Main Roads WA has a well-defined set of quality check rules. Using the above three traffic volume statistics for illustration, the minimum number of statistics for a site to be treated as active is as follows: At least one weekday must exist for MADW to be calculated for that weekday in the month, e.g. at least one Monday out of four or five Mondays in a month; At least nine MADWs for a weekday must exist before AADW is calculated for that weekday (i.e. nine out of twelve); All seven AADWs must exist before AADT is calculated for a Pattern Count Site (PCS); In all other circumstances, if a site has insufficient data, it will be treated as inactive unless missing data is estimated. Other similar rules apply to average weekday traffic and other related quantities when Saturdays, Sundays and public holidays are not considered.
Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005 Austroads 2004 — 43 — Best Practices in Road Use Data Collection, Analysis and Reporting A PCS can exceed the minimum data requirement and still be unsuitable to calculate AADT or be used in seasonal adjustment. Sites which have unusual or significant seasonal variations fall into this category. Therefore all data for all PCS must be reviewed prior to acceptance of the data and production of final AADT and seasonal factors. The initial procedures are as follows: Identify and remove all anomalous data. Re-run monthly/ annual reports and identify which sites have AADT affected by missing data. Identify which of these sites and months are required for seasonal adjustment. These sites/months will have estimates submitted. Estimates will be submitted to meet minimum data requirements and to produce seasonal factors for months where associated short-term data requires adjustment. Estimate MADW data for these sites. It is not necessary to submit estimates for missing data where the minimum requirement is already achieved, seasonal factors are not required and the missing data has the same result as the MADW. For example, a PCS has nine MADW values and July’s MADW for Monday is missing. If the AADW for Monday is 6225 and the estimated MADW for July is 6228 then it is not necessary to submit this MADW. If there is no data for a specific weekday in a month where estimates are required then an MADW must be submitted. If an actual MADW exists for a specific weekday for a month but this has been derived from less than the minimum number of those weekdays required in that month, then the MADW must be reviewed and where required, an estimate will replace the actual value. Public holidays have a significant effect on MADW. Any months with missing daily flows for weekdays on which public holidays occur (particularly Mondays) will usually require an estimate to replace the actual value. For the estimation of missing data, it is necessary to refer to historical information, current information for previous and subsequent weeks and available data for the pattern relating to weekday flows. Estimation of the value is either by computer modelling or manual means using graphical analysis. The framework of the RTA-CSIRO software for detecting and correcting corrupt data is shown in Figure 13. It fits a linear model on all available data as follows (Donnelly 2003): Predicted value = grand median + trend + season + day + hour + school + public The model then uses a Hidden Markov Model (HMM) to detect periods of erratic counts. If any detected period exceeds seven days then that period is removed from the dataset in the HMM modelling process. A second linear model is fitted to the remaining data and outlier detection method is used to detect and correct individual extreme counts. If no periods of erratic counts are detected then the outlier method can be run using the original linear model. The second linear model is used to predict counts that are missing from the dataset, either because they were removed during this process (before linear modelling or during the HMM process) or because they were missing from the original data. In summary, the management of missing data still requires substantially more research. A vital point is to flag amended data and ensure that the original raw data sets can be recovered should better estimation techniques become available. There is much expectation that the CSIRO software developed for RTA NSW would soon be in production phase and become useful also to other RAs.
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AP-G84/04 Best practice in road use data collection, analysis ... - WIM

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