M Slavik & J Bosman

2 Expertise Data Information Knowledge M1 M2 M3

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

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

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)

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

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

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

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

STEP 4 –TRAFFIC LOADING MODELS TypeLT class Law Enf. 1Below 35 % Any 235 % – 55 % Weak 335 % – 55 % Strong 4Over 55 % Weak 5Over 55 % Strong

STEP 5 – FIVE MODEL STATIONS TypeLT LE Model 1 Below 35 % Any N12 Kliprivier 2 35 % – 55 % Weak N2 Winkelspruit 3 35 % – 55 % Strong N4 Komati 4Over 55 % Weak N3 Hidcote 5Over 55 % Strong N3 Heidelberg

TABLE 2. Traffic and Sample Sizes at the Five Traffic-loading Model Stations TypeAbbrev.ADT/dirADTT/dirHVHV-ax 1 KLP WNK KMT HDC HDB

TABLE 3. Key Figures of the Five Traffic-loading Types Model %LT classLEWIM% LTt/axE80/axax/HV E80/HV 1 Below 35AnyKLP WeakWNK StrongKMT Over 55WeakHDC Over 55StrongHDB

1 - N12 KLIPRIVIER

2 - N2 WINKELSPRUIT

3 - N4 KOMATIPOORT

4 - N3 HIDCOTE

5 - N3 HEIDELBERG

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

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.

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.

M Slavik & J Bosman

FACTS: Bad news: Too dispersed - bottom of a bird’s cage Good news: Red line above green line All lines rising from left to right Blue line joins green on left, red on right

TABLE 1. Five Traffic-loading Model WIM Stations TypeWIMAbbrRoadDirectionLanes/dirCTO no 1KliprivierKLP nbN12northbound WinkelspruitWNK nbN2northbound KomatiKMT ebN4eastbound HidcoteHDC sbN3southbound HeidelbergHDB sbN3southbound33059