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Archived Data Management System

This document provides a preliminary data analysis of the 2002 Average Annual Daily Traffic (AADT) estimation study conducted by the Archived Data Management System Study Advisory Committee. It discusses the TRIMARC and ARTIMIS procedures used for data extraction and screening, problems encountered, and potential solutions. The document also outlines the process of summarizing volume data and calculating AADT using different methods. Quality control criteria, data screening summaries, and AADT estimates at sample sites are provided, along with observations and conclusions.

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Archived Data Management System

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  1. Archived Data Management System Study Advisory Committee Meeting May 14, 2003

  2. Preliminary Data Analysis 2002 AADT Estimation • TRIMARC • ARTIMIS

  3. Procedure • Data extraction and screening Applied quality check criteria used in mobility monitoring study by TTI Problem encountered: The rounding of occupancy in the segment file created problems. Potential solution: Eliminating the vehicle length criteria

  4. Procedure (cont’d) • Summarize 15min volume to hourly volume • If there are at least 2 15min records, calculate hourly volume by adding or extrapolating; • Otherwise, mark hourly volume as missing/null.

  5. Procedure (cont’d) • Summarize hourly volume to daily volume • If there are 24 hourly volume records for a day, add them. • If there are 18-23 hourly volume records for a day, impute the hourly volumes for those “missing” hours based on hourly distribution at the same site throughout the year, and then add them. • Otherwise, mark the day as missing/null.

  6. Procedure (cont’d) • For each site, screen out daily volumes on those days of week based on ±1.5σ from the day-of-week mean. • Calculate day-of-week volume distribution using yearly average for each day of week.

  7. Procedure (cont’d) • Calculate AADT using the AASHTO formulation (I) where: • VOL = daily traffic for day k, of day-of-week i, and month j • i = day of the week • j = month of the year • k = 1 when the day is the first occurrence of that day of the week in a month, 4 when it is the fourth day of the week. • n = the number of days of that day of the week during that month (usually between 1 and 5, depending on the number of missing data).

  8. Quality Control Criteria

  9. Quality Control Criteria (cont’d)

  10. Quality Control Criteria (cont’d)

  11. Data Screening Summary

  12. Data Screening Summary (cont’d)

  13. TRIMARC Data Quality

  14. ARTIMIS Data Quality

  15. Alternative Methods • Calculate Monthly ADT (MADT) and multiply it with the monthly factor to get AADT (II) • Estimate MADT using two-week’s of “good” data (with minimum 15min records marked as missing/null) and multiply it with the monthly factor to get AADT (III)

  16. 2003 Monthly Factor (Division of Planning, KYTC)

  17. TRIMARC Summary (Method I)

  18. TRIMARC Summary (Method II)

  19. TRIMARC Summary (Method II) (cont’d)

  20. TRIMARC Summary (Method II) (cont’d)

  21. TRIMARC Summary (Method II) (cont’d)

  22. TRIMARC Summary (Method II) (cont’d)

  23. ARTIMIS Summary (Method I)

  24. ARTIMIS Summary (Method II)

  25. ARTIMIS Summary (Method II) (cont’d)

  26. ARTIMIS Summary (Method II) (cont’d)

  27. ARTIMIS Summary (Method II) (cont’d)

  28. AADT Estimates at Sample Sites

  29. Observations • Data availability varied by sites in 2002. • The rounding of occupancy data may have caused large amount of data being screened out by vehicle length criteria, which were hence dropped when processing ARTIMIS data.

  30. Observations (cont’d) • Generally, the AADT estimates obtained from method II are closer to the State figure when more valid days are present for a month. • Significant differences exist between AADT estimates obtained using monthly factors for all week, weekday, and weekend (method III).

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