TRAFFIC LOADING ESTIMATED FROM COUNTS

1. TRAFFIC LOADING ESTIMATED FROM COUNTS M Slavik & J Bosman

2. Data Expertise Information M1 M2 M3 Collect 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Collect New Data Organise Make Accessible Analyse Apply Retrieve Knowledge 2

3. 70 % freight by road • 4 % p.a. growth • Information on traffic loading • needed for: • pavement design • road maintenance • law enforcement • statistics, patterns, trends

4. SOURCES OF TRAFFIC LOADING • INFORMATION: • Inductive-loop counters • Weigh-In-Motion (WIM) • Weighbridges

5. ASPECTS OF TRAFFIC LOADING: • Magnitude • Average Daily Traffic (ADT) • Average Daily Truck Traffic (ADTT) • Composition • LV, HV; HV - short, medium, long, • buses, vehicle / axle configuration • Axle loads • Axle-load distribution • ESAL (E80)

18. E80 FROM INDUCTIVE LOOPS • Bosman 1988: • 4 classes of road, by % of 2-ax HV • Typical (default) axle-load distributions • Bosman 2004: • Simplified to 3 classes • Slavik & Bosman in 2006: • Parameters measurable by loops • HV - Short, Medium, Long, vs E80/HV • Influence of law enforcement • 3 steps

19. STEP 1 – RELATION WITH E80/HV • Loop measured attributes: • % heavy vehicles (HV) • % short HV • % mediun HV • % long HV

20. STEP 2 – 76 WIM STATIONS, 2005 • Data validated • Reprocessed • % Long trucks determined • E80/HV evaluated

21. STEP 3 – LAW ENFORCEMENT • INTENSITY : • Strong – permanent presence • Medium – ad-hoc, blitzes • Weak – occasional; non-existent • STEPS 1 + 2 + 3 : GRAPH – FIG.1

23. STEP 4 –TRAFFIC LOADING MODELS Type LT class Law Enf. 1 Below 35 % Any 2 35 % – 55 % Weak 3 35 % – 55 % Strong 4 Over 55 % Weak 5 Over 55 % Strong

24. STEP 5 – FIVE MODEL STATIONS Type LT LE Model 1Below 35 % Any N12 Kliprivier 235 % – 55 % Weak N2 Winkelspruit 335 % – 55 % Strong N4 Komati 4Over 55 % Weak N3 Hidcote 5Over 55 % Strong N3 Heidelberg

25. TABLE 2. Traffic and Sample Sizes at the Five Traffic-loading Model Stations

26. TABLE 3. Key Figures of the Five Traffic-loading Types

27. 1 - N12 KLIPRIVIER

28. 2 - N2 WINKELSPRUIT

29. 3 - N4 KOMATIPOORT

30. 4 - N3 HIDCOTE

31. 5 - N3 HEIDELBERG

35. WHY? • Weak relationship between %LT and E80/HV • Imprecision of WIM due to • - Calibration problems • - Deteriorating pavement • - Hardware and software defects • No cheap substitute for good WIM measurements • NEXT? • Strict WIM quality control (European Standard) • Uniform data validation procedures • Uniform tender requirements • Re-appraise situation in 2-3 years time

36. CONCLUSION • The relationship between the percentage of long trucks and E80/HV is not very good. R-square varies from 0,16 to 0,66. • The trend lines, however, indicate that • the E80/HV is lower with higher law enforcement, and • the E80/HV is higher with a higher percentage of long trucks. It is thus recommended that, in the absence of better traffic loading data • the E80/HV values in Table 3 of the paper, and • the axle distributions in Appendix A of the paper • be used by designers and practitioners in the meantime.

37. ACKNOWLEDGEMENTS • The authors wish to express their gratitude to: • NTRV (Northern Toll Road Venture, the N1 Toll Road Concessionaire) • N3TC (N3 Toll Concession, the N3 Toll Road Concessionaire), • TRAC (Trans African Concessions, the N4 Toll Road Concessionaire), • Bakwena (the N4 Platinum Toll Road Concessionaire), and • SANRAL (South African National Roads Agency Limited) • for the traffic data and information made available.

38. TRAFFIC LOADING ESTIMATED FROM COUNTS M Slavik & J Bosman