Developing the autom atic m easurem ent of surface condition on local roads Alex W right Alex W right • • TRL I nfrastructure Division TRL I nfrastructure Division • • Group m anager, Technology Technology Developm ent Developm ent • Group m anager, • m w right@trl.co.uk • m w right@trl.co.uk • Portorož, Slovenia
Measuring condition at traffic-speed in the UK o UK condition surveys measure • Longitudinal profile • Transverse profile • Texture profile • Cracking (automatic) • Geometry o Annual coverage • TRACS: 40,000km motorway and trunk roads • SCANNER: 80,000km local road network o Surveys carried out to an end result specification Portorož, Slovenia
“UK” System s • Accredited Systems: • Jacobs – Ramboll RST26, RST27 • WDM – RAV1, RAV2, RAV3, RAV4 • DCL – Roadware ARAN1, ARAN2 Portorož, Slovenia
UK trunk roads - TRACS Portorož, Slovenia
UK local roads ( rural) - SCANNER Portorož, Slovenia
UK local roads ( urban) - SCANNER Portorož, Slovenia
Use of the Data o Local use • Parameters reported over 10m lengths for local use o Network use • For trunk roads total length of poor values reported • Single HA performance indicator (PI) • For local roads a Road Condition Index (RCI) is produced every 10m 90 80 • Reports “overall” condition score 70 Proportion (%) 60 Red 50 • Distribution of RCIs over the local Amber 40 Green 30 authority defines network condition 20 10 (LA Indicator) 0 A • Potential use in allocation of funding Road category across authorities Portorož, Slovenia
Enhancing the use of data from local roads o Local roads differ from trunk roads o New methods required to maximise value of local road data o Research to improve the use of the survey data • Measuring ride quality on local roads using shape data • Using texture to assess surface deterioration on local roads • Measuring edge deterioration on local roads o Work concentrated on the use of shape data o Began with consultation to find out what users needed in practice Portorož, Slovenia
“Shape” data collected at traffic-speed 75 74 73 72 71 70 69 68 67 66 65 Chainage (m) Portorož, Slovenia
Measuring ride quality on local roads - consultation o Consultation with engineers found that • Little importance placed on longitudinal profile data • Key structural measure is cracking and rutting • Engineers desire a reliable assessment of general ride quality (functionality) • But engineers key concern is defects giving rise to bumps (user complaints) o Concluded that methods needed to • Reliably identify lengths with poor ride quality • Identify general locations giving rise to bumps Portorož, Slovenia
Measuring ride quality - data collection o A practical investigation to relate surface profile to user opinions on local roads o Several routes surveyed, including sections known to be poor o Profile data provided by HARRIS1 profilometer • Measurements in both wheel tracks (and across survey width) o User surveys: • Car surveys • Motorbike survey • Utilising on-board data collection with GPS referencing • Reported on ride and bumps • Repeat surveys for consistency Portorož, Slovenia
Considering general ride quality 1000 o Wavelet Decomposition 1.38-1.4 100m lengths where dial >2 100m lengths where dial <=2 1.35-1.38 42650 42640 1.33-1.35 1 42630 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.3-1.33 42620 42610 Power 1.28-1.3 42600 0.001 42590 1.25-1.28 42580 1m – 5m 42570 1.23-1.25 42560 0.000001 42550 Site Chainage (m) 1.2-1.23 42540 1.18-1.2 o PSD 42530 3m 5m 42520 1.15-1.18 0.000000001 42510 Wavelength 42500 1.13-1.15 42490 42480 1.1-1.13 o IRI, Ride Number, Profile Index 42470 1.08-1.1 42460 42450 1.05-1.08 o MA and enhanced variance 42440 42430 1.03-1.05 42420 o Coefficient de planeite 42410 1-1.03 42400 0.375916 0.495487 0.653091 0.860827 1.134638 1.495543 1.971245 2.598258 3.42471 4.514041 5.949865 7.842396 10.336902 0.98-1 o Waveband Energy 0.95-0.98 Wavelength (m) 0.93-0.95 o Standard Deviation Portorož, Slovenia
General ride quality - w avelength response o I RI o 3 m Variance Portorož, Slovenia
Param eter for general ride quality o Predicting general ride quality on local roads • 1-5m wavelength features cause the users most discomfort. • 3m enhanced variance agreed best with user opinion of underlying ride quality. Other measurements agreed no better with the user’s opinion. • 10m enhanced variance showed some agreement (effects of longer wavelengths on truck drivers). • Wavelengths over 20m - little or no agreement with user o Effect of measurement (line) • Offside measurements contributed to 33% of agreement with user opinion. • Multiple measurement lines around the wheelpath did not improve agreement Portorož, Slovenia
Measuring “Bum ps” on local roads o User surveys recorded bumps using button presses o Wavelet analysis suggested wavelengths of interest lie between 1 and 3m. o Existing measurements (variance, IRI etc) did not reliably report the locations of the features causing this bump- like discomfort. 4 3.8 Button press 3.6 No button press 3.4 Normalised Power 3.2 3 2.8 2.6 2.4 0.5m 2.2 2.5m 2 0 5 10 15 20 25 30 35 40 45 Wavelength (m) Portorož, Slovenia
Measuring “Bum ps” on local roads 7 6 5 Dial value 4 3 2 1 C at4 3m enh var NS W T 0 C at 4 3m enh var O S W T 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 Dial value C h a in ag e (m ) B um p Portorož, Slovenia
A param eter for “Bum ps” on local roads o Considered many approaches, e.g. • 1.25m enhanced variance, change of vehicle acceleration, derivative of longitudinal profile (features too small to impact on a car’s tyre) o The Central Difference Method • Calculates a “derivative” for each point along the road (profile measurements {yi}, taken at distances {xi} along the road): − = y y + − ' ( ) F x i 1 i 1 i − x x + − 1 1 i i • Similarly for F’’. • The maximum of these values is calculated over 1m lengths. • If max(F’) and max(F’’) both exceed set thresholds, then the length contains a bump and a value of “1” is reported for that length. Otherwise “0” is reported. Portorož, Slovenia
Measuring “Bum ps” w ith the CDM – local roads o Tests to review locations where the bump measure responded • Reported 84% of user button presses. • Potential high number of false positives. • Inspection of 3D profile and video showed features of note where CDM responds, but users had not always pressed the button. o Concluded • This is an appropriate method for identifying “bumps”. • We should use a combination of this and 3m enhanced variance for assessing general ride and bump density on local roads Portorož, Slovenia
Testing on trunk roads Easting and Northing 182500 172500 Northing 162500 152500 465000 470000 475000 480000 485000 490000 495000 500000 505000 510000 515000 Easting Portorož, Slovenia
Measuring “Bum ps” – trunk roads o Applied to whole of trunk road and motorway network. • 0.17% of network reported to contain bumps o Subset inspected in closer detail: • Inspected 3D profile for 10% of locations • Visual inspection on site of 1% of locations o Where 3D profile inspected: • 87% contained obvious bumps • Further 10% showed general unevenness o Where site inspected, • 64% showed visible bumps on site • 24% were not “bumps”, but were poor bridge joints • 3% were bumps at surface change Portorož, Slovenia
Measuring Edge deterioration - consultation o Consultation with engineers found that • Edge deterioration universally considered an area for concern • Key requirement for a measure to aid in defining maintenance treatment o Features of interest • Potholes in surface near edge • Overriding • Cracking of surface near edge • Edge supported or kerbed • Presence of patching Portorož, Slovenia
Developing param eters for Edge Deterioration o A fully automated measure o Utilising transverse profile data • Firstly Identify the edge strip o Edge Roughness • Roughness within the edge strip o Edge Stepping • Stepping at the nearside of the edge strip o Transverse Variance • Assessing roughness across the pavement Portorož, Slovenia
Edge deterioration param eters Portorož, Slovenia
The Edge deterioration param eters o Transverse edge roughness edge step unevenness Portorož, Slovenia
Testing the Edge deterioration param eters 30 1 0.9 CVI Edge Deterioration, average severity over 1km Number of 10m lengths in each 1km exceeding 25 0.8 0.7 20 95th percentile level 0.6 0.5 15 0.4 10 0.3 0.2 5 0.1 0 0 0 20 40 60 80 100 C h a in a g e , k m E dge S tep L2 E dge S tep L1 C V I-B _E D Portorož, Slovenia
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