USE OF WIM IN SOUTHERN AFRICA Current / Future Louw Kannemeyer 2 - - PowerPoint PPT Presentation

USE OF WIM IN SOUTHERN AFRICA Current / Future

1 Louw Kannemeyer

Contents

• Road Network
• Current WIM Use
• Future WIM Use

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Authority Paved Gravel Total

SANRAL 22 214 22 214 Provinces - 9 46 548 226 273 272 821 Metros - 8 51 682 14 461 66 143 Municipalities 37 680 219 223 256 903

Total 158 124 459 957 618 081

Un-Proclaimed (Estimate) 131 919 131 919

Estimated Total 158 124 591 876 750 000

SA ROAD NETWORK - 2018

Un-Proclaimed Roads = Public roads not formally gazetted by any Authority

World 64 285 009 1 United States 6 586 610 2 India 4 689 842 3 China 4 237 500 4 Brazil 1 751 868 5 Japan 1 210 251 6 Canada 1 042 300 7 Russia 982 000 8 France 951 200 9 Australia 823 217 10 South Africa 750 000 11 Spain 681 298 12 Germany 644 480 13 Sweden 572 900 14 Italy 487 700 15 Indonesia 437 759 16 Turkey 426 906 … … … 34 Dem Rep of Congo 153 497 45 Zimbabwe 97 267 54 Zambia 91 440 55 Tanzania 91 049 70 Madagascar 65 663 80 Angola 51 429 72 Namibia 64 189 98 Mozambique 30 331 104 Botswana 25 798 122 Malawi 15 451 148 Lesotho 7 438 161 Swaziland 3 594 173 Mauritius 2 066 193 Seychelles 508 1 449 720 Rank Country Road length (km) SADC Total

South Africa has the 10th longest total and 18th longest paved road network in the world The National Development Plan states that roads represent one of the largest public infrastructure investments in most countries. RSA road replacement cost >R2 trillion

Freight flow on road and rail (10th State of Logistics Survey 2014) Also important to note that of the person trips recorded in National Household Travel Survey, 2013, by transport modes are as follow:

• Minibus taxi’s (41.6%)
• Private Vehicles (23.4%)
• Walking (18.5%) – Along road corridors
• busses (10.2%)
• Trains (4.4%)
• Other (1.9%)

Roads account for 87.9% of Freight and 93.7% of Person Trips

Mode Choice Factor Percentage Travel time 32.6 Travel Cost 26.1 Flexibility 9.2 Other 32.1

SOUTH AFRICA ROAD USE

Total Life Cycle Transportation Costs

Road User Cost is up to 90% of Total Life Cycle Transportation Cost

Very Good Very Poor

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SANRAL Traffic Monitoring Stations

Accurate Traffic Data – Most Important Data Item Capacity Analysis / Pavement Design / Life Cycle Economics / Toll Income

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Traffic Monitoring Stations - WIM

Current Active WIM Stations

Typical RSA WIM Station

• Main Problem - Systematic deviations in WIM observations

due to quality/calibration of WIM installation.

• Available Calibration Methods
• On-site calibration of WIM equipment
• Automatic self-calibration
• Post-processing calibration
• Why post-processing calibration?
• Difficult to undertake full-scale on-site calibration (sample/weigh bridge)
• WIM calibration tends to “drift” over time
• Post-calibration method applied after load measurements. Can be

reapplied to old data.

• “Truck-Tractor” (TT) method - Calibration based on load observations of

population sample of articulated trucks of a certain type and size

• Development of method – Dr Martin Slavik/Mr Gerhard de Wet

WIM – Systematic Deviations

21.8 tons

Poor WIM Installation

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Good WIM Installation

WIM - Random Deviation

• Axle load distribution
• WIM Random errors and variation in dynamic loads result in:
• Measured axle distribution wider than actual static load distribution
• Particularly at higher end of distribution
• Results in overestimation of percentage “overloaded” axles
• Basic adjustment methodology
• Observed axle load measurements is the sum of
• Static load of the axle plus
• WIM error and dynamic impact
• If information on WIM error and dynamic impact is known
• Then such impact can be “subtracted” from observed axle loads
• To provide the static load of the axle

Random Deviation Correction Important When Quantifying Overload Damage

• “Expectation-Maximization-Smoothing” (EMS) algorithm
• Applies a numeric technique using so-called “deconvolution” method
• Wim errors basically “convolutes” or distorts the static load
• Deconvolution removes this convolution from data
• Central limit theorem is a special case
• Numeric method does not require fitting of Log-Normal distributions
• Can also be solved by means of Expectation-Maximization
• Problem is that deconvolution is very sensitive to “noise” in data
• Can only be used when data relatively free of noise
• This problem is solved by incorporation of smoothing algorithm
• Smoothing intended to remove noise from data

WIM - Random Deviation

0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Obs Dist Corrected Fitted Dist Corr Fitted

WIM - Random Deviation

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SANRAL OVERLOAD SOFTWARE

15 to 30 % Vehicles Overloaded – Only 2% loaded beyond Prosecution Grace Statistics - Screened Sample versus Population

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SANRAL OVERLOAD SOFTWARE

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Committee of Transport Officials (COTO) TMH Standards

FUTURE WIM USE

• Pavement Design/Maintenance
• Old – Axle Load Histogram reduced to Equivalent Standard

Axle Load per vehicle - E80

• Future – SARDS Complete Axle Load Histograms used along with Tyre

Contact Stress (How load is transferred to Pavement !!!)

20 20 (Not to scale) n-shape:

• Single

Circular

n-shape:

• Single

rectangular m-shape:

• Triple

rectangular

FUTURE WIM USE

• Overload Control
• Old – Screeners at Static Weigh Bridges
• 50 to 100 km impact radius
• Construction/Operational Costs
• Human Factor
• Future – WIM-Enforcement
• Direct Weight Enforcement integrate with Average

Speed over Distance (ASOD) – 250+ Installations

• Been trialled over past 5 years
• Awaiting National Regulator Compulsory Standards

Type Approval for ASOD and WIM-E

• End 2018
• Realtime Integration to SANRAL Central Operations

Centre

• Realtime Tracking of Load Movements Country Wide

(OD)

• Direct Enforcement (Speed/Load)
• Insurance Fraud
• Security Applications
• Abnormal Permits Enforcement
• Industry Self Regulation Verification
• ???

21 21 (Not to scale)

THANK YOU

Louw Kannemeyer Engineering Executive SANRAL louwk@nra.co.za www.sanral.co.za

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