M Slavik & J Bosman

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DataData

Information

KnowledgeKnowledgeM1 M2 M3M1 M2 M3

70 % freight by road 4 % p.a. growth

Information on traffic loadingneeded for:

• pavement design• road maintenance• law enforcement• statistics, patterns, trends

SOURCES OF TRAFFIC LOADINGINFORMATION:

• Inductive-loop counters

• Weigh-In-Motion (WIM)

• Weighbridges

ASPECTS OF TRAFFIC LOADING:

• MagnitudeAverage Daily Traffic (ADT)Average Daily Truck Traffic (ADTT)

• CompositionLV, HV; HV - short, medium, long,buses, vehicle / axle configuration

• Axle loadsAxle-load distributionESAL (E80)

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Axle load, t

E80

E80 = (Axle load / 8.2)4.2

E80 FROM INDUCTIVE LOOPS

• Bosman 1988:4 classes of road, by % of 2-ax HVTypical (default) axle-load distributions

• Bosman 2004: Simplified to 3 classes

• Slavik & Bosman in 2006:Parameters measurable by loopsHV - Short, Medium, Long, vs E80/HVInfluence of law enforcement3 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 1 + 2 + 3 : GRAPH – FIG.1

E80/HV vs %LT and LE - ALL DATA

y = 0.0243x + 1.3303

R2 = 0.1604

y = 0.0414x + 0.235

R2 = 0.6557

y = 0.0263x + 0.6066

R2 = 0.3106

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

20 25 30 35 40 45 50 55 60 65 70 75 80 85

Percentage of long trucks

E8

0 p

er

he

avy

veh

icle

Strong LE Med LE Weak LE Linear (Weak LE) Linear (Med LE) Linear (Strong LE)

1 KLPnb

2 WNKnb

3 KMTeb

5 HDBsb

4 HDCsb

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

STEP 5 – FIVE MODEL STATIONS

Type LT LE Model

1 Below 35 % Any N12 Kliprivier

2 35 % – 55 % Weak N2 Winkelspruit

3 35 % – 55 % Strong N4 Komati

4 Over 55 % Weak N3 Hidcote

5 Over 55 % Strong N3 Heidelberg

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

Type Abbrev. ADT/dir ADTT/dir HV HV-ax

1 KLP 32 451 1 618 367 147 1 512 495

2 WNK 11 898 951 317 918 1 444 444

3 KMT 1 619 222 46 932 210 335

4 HDC 7 223 1 995 593 819 3 154 415

5 HDB 5 082 1 052 325 177 1 703 140

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

Model %LT class LE WIM % LT t/ax E80/ax ax/HV E80/HV

1 Below 35 Any KLP 29.8 4.845 0.411 4.12 1.69

2 35 - 55 Weak WNK 42.2 5.080 0.574 4.54 2.61

3 35 - 55 Strong KMT 48.9 5.279 0.415 4.48 1.86

4 Over 55 Weak HDC 60.6 5.984 0.583 5.31 3.10

5 Over 55 Strong HDB 58.4 5.783 0.453 5.24 2.37

DISTRIBUTION OF RAW AXLE LOADSLANE 1

E80/HV = 1.69; Model 1; KLPnb; %LT<35 - Any LE; 1512495 HV axles (8.5ms27/2/06)Averages: ton/AL=4.845; E80/AL=0.411; Axles/HV=4.120; E80/day=1855.2; SD=2.625

Axle load, t20191817161514131211109876543210

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Frequency, %

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8

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0

1.69

8.91

19.78

14.89

10.8911.3

9.4

8.08

6.85

4.61

2.24

0.870.31 0.11 0.04 0.01 0 0 0 0

1 - N12 KLIPRIVIER

DISTRIBUTION OF RAW AXLE LOADSLANE 1

E80/HV = 2.61; Model 2: WNKnb; 35<%LT<55 - Weak LE; 1444444 HV axles (8.6ms27/6/06)Averages: ton/AL=5.080; E80/AL=0.574; Axles/HV=4.543; E80/day=2363.9; SD=2.972

Axle load, t20191817161514131211109876543210

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Frequency, %

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8

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1.27

11.46

19.32

13.5

10.63

9.48

6.56

5.66

7.457.05

4.61

2.04

0.70.21 0.06 0 0 0 0 0

2 - N2 WINKELSPRUIT

DISTRIBUTION OF RAW AXLE LOADSLANE 1

E80/HV = 1.86; Model 3; KMTeb; 35<%LT<55 - Strong LE; 210335 HV axles (8.5ms27/2/06)Averages: ton/AL=5.279; E80/AL=0.415; Axles/HV=4.482; E80/day=402.2; SD=2.409

Axle load, t20191817161514131211109876543210

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Frequency, %

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9

87

6

54

3

21

0

0.65

5.63

14.49

15.99

11.68 11.4211.03

11.86

10.39

5.24

1.3

0.24 0.06 0.01 0.01 0.00 0 0 0 0

3 - N4 KOMATIPOORT

DISTRIBUTION OF RAW AXLE LOADSLANE 4

E80/HV = 3.10; Model 4; HDCsb; %LT>55 - Weak LE; 3154415 HV axles (8.5ms27/2/06)Averages: ton/AL=5.984; E80/AL=0.583; Axles/HV=5.312; E80/day=5151.2; SD=2.438

Axle load, t20191817161514131211109876543210

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Frequency, %

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9

8

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3

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1.28

2.54

8.69

12.18

9.95

14.23 14.46

12.01

12.95

7.41

3.42

0.720.12 0.03 0.01 0.00 0 0 0 0

4 - N3 HIDCOTE

DISTRIBUTION OF RAW AXLE LOADSLANE 1

E80/HV = 2.37; Model 5; HDBsb; %LT>55 - Strong LE; 1703140 HV axles (8.5ms27/2/06)Averages: ton/AL=5.783; E80/AL=0.453; Axles/HV=5.238; E80/day=2141.4; SD=2.112

Axle load, t20191817161514131211109876543210

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Frequency, %

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8

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00.15

1.58

8.85

13.82

12.59

14.23

17.31

14.67

10.64

4.77

1.12

0.22 0.04 0.01 0.00 0.00 0 0 0 0

5 - N3 HEIDELBERG

BASE-YEAR PRESENT WORTH OF PAVEMENT LIFE COST

Model 4: %LT>55, Weak LEAverage = 437.48 R/m2; St.dev. = 38.60 R/m2

Base-year present worth of life cost C, R/m2600580560540520500480460440420400380360340

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(5.19/9/06)

BASE-YEAR PRESENT WORTH OF PAVEMENT LIFE COST

Model 5: %LT>55, Strong LEAverage = 393.90 R/m2; St.dev. = 28.29 R/m2

Base-year present worth of life cost C, R/m2500480460440420400380360340320

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(5.19/9/06)

E80/HV vs %LT and LE - ALL DATA

y = 0.0243x + 1.3303

R2 = 0.1604

y = 0.0414x + 0.235

R2 = 0.6557

y = 0.0263x + 0.6066

R2 = 0.3106

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

20 25 30 35 40 45 50 55 60 65 70 75 80 85

Percentage of long trucks

E8

0 p

er

he

avy

veh

icle

Strong LE Med LE Weak LE Linear (Weak LE) Linear (Med LE) Linear (Strong LE)

1 KLPnb

2 WNKnb

3 KMTeb

5 HDBsb

4 HDCsb

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