USE OF WIM IN SOUTHERN AFRICA Current / Future Louw Kannemeyer

Contents Road Network Current WIM Use Future WIM Use

SA ROAD NETWORK - 2018 Authority Paved Gravel Total 158 124 459 957 SANRAL 22 214 Provinces - 9 46 548 226 273 272 821 Metros - 8 51 682 14 461 66 143 Municipalities 37 680 219 223 256 903 158 124 459 957 618 081 Un-Proclaimed (Estimate)   131 919 Estimated Total 591 876 750 000 Un-Proclaimed Roads = Public roads not formally gazetted by any Authority

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

Roads account for 87.9% of Freight and 93.7% of Person Trips SOUTH AFRICA ROAD USE 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%) Mode Choice Factor Percentage Travel time 32.6 Travel Cost 26.1 Flexibility 9.2 Other 32.1 Roads account for 87.9% of Freight and 93.7% of Person Trips

Road User Cost is up to 90% of Total Life Cycle Transportation Cost Total Life Cycle Transportation Costs Very Good Very Poor Road User Cost is up to 90% of Total Life Cycle Transportation Cost

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

Current Active WIM Stations Traffic Monitoring Stations - WIM Current Active WIM Stations

Typical RSA WIM Station

WIM – Systematic Deviations 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 South African research has shown that significant errors can be found in Weigh-In-Motion observations due to calibration issues with WIM equipment. Various calibration were investigated over many years in the country resulting in the development of the so-called Truck-Tractor method The method has now been extensively tested in South Africa and has been found to be fairly accurate It is a post-calibration method that can be used after load measurements have been undertaken. It therefore has the advantage that it can be applied to previous WIM measurements. SANRAL has been collected axle load data for nearly two decades and the method can therefore also be applied to such data, which is a major advantage of the method. The calibration is based on the load observations of articulated trucks of a certain type and size typically found on South African roads. 10. CAPSA 2015 - Traffic Modelling & Contact Stress-MDB.pptx 21.8 tons

Poor WIM Installation

Good WIM Installation

Random Deviation Correction Important When Quantifying Overload Damage 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

WIM - Random Deviation “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

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

SANRAL OVERLOAD SOFTWARE

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 !!!) m-shape: - Triple rectangular n-shape: - Single rectangular n-shape: - Single Circular 20 (Not to scale)

FUTURE WIM USE Overload Control 21 (Not to scale) 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 (Not to scale)

Engineering Executive SANRAL THANK YOU Louw Kannemeyer Engineering Executive SANRAL louwk@nra.co.za www.sanral.co.za 22