Road condition surveys using smartphonesMonitoreando la condición de camino con el uso de

teléfonos inteligentesLars Forslof - CEO/founder - Road engineer

Sweden - Suecia• 9 million inhabitants

– 80% of population in south

– Stockholm - Capital 1,5 million

• Industry– Minerals and forest industry

– Volvo, Scania, Sandvik, IKEA, ABB, SKF, Ericsson

– Design/Architecture/Music/Tourism

• High level of new innovations– as Bluetooth, Spotify, Skype

• Infrastructure challanges– Low populated areas in north

– Winters: -30° celsius and > 1 meter snow

– Frost heave/thaw in spring

Smartphone -> IRI -> RMMS

www Exports: IRI in 20 -200 mHDM4, RMMS, PMS, SIEM

GIS/Shape, ex: SIGVial

Short history

2003-2004 PC / GPS / External accelerometers, MATLAB

2004-2006 ”Auto PC” win98, External accelerometers, C++

2010- Smartphone/app revolution, all needed in built-in

Roadclass Morocco % Cambodia % Sri Lanka %

Good 12713 87,6 114640 59,9 12956 45,3

Satisfasctory 7,9 7,9 21643 11,3 2015 7,1

Unsatisfactory 356 2,5 13770 7,2 2108 7,4

Poor 291 2 42011 21,1 11492 40,2

MEAN Value 1.19 1.91 2.42

S:a 14511 192064 28571

Från Till Hans Bo S Kr. W Hossein1 Hossein2 Hans2 L-E H Robin Kalle Medel 0 100 1 1 1 1 1 1 2 1 1 1,11

100 200 2 1 1 2 2 2 2 2 2 1,78 200 300 2 1 1 1 1 1 2 1 1 1,22 300 400 2 1 2 2 2 2 2 2 2 1,89 400 500 2 2 2 2 2 2 2 3 2 2,11 500 600 1 1 1 1 1 1 1 1 1 1,00 600 700 1 1 2 1 2 2 2 2 2 1,67 700 800 3 2 3 3 3 3 3 3 3 2,89 800 900 2 2 1 1 2 2 2 2 1 1,67 900 1000 2 2 1 2 3 2 2 2 2 2,00

1000 1100 1 1 1 2 1 1 1 1 2 1,22 1100 1200 1 1 1 2 1 1 1 1 1 1,11 1200 1300 1 2 2 1 1 1 1 1 2 1,33 1300 1400 2 2 3 2 3 2 3 2 3 2,44 1400 1500 2 2 2 3 4 3 2 3 3 2,67 1500 1600 1 1 2 1 2 1 2 1 1 1,33 1600 1700 2 2 2 3 3 2 2 2 2 2,22 1700 1800 2 1 1 3 3 2 1 2 2 1,89 1800 1900 1 1 1 1 1 1 1 1 1 1,00 1900 2000 2 1 1 1 2 1 1 1 1 1,22 2000 2100 2 2 1 1 2 2 2 2 1 1,67 2100 2200 2 2 1 2 2 1 1 1 2 1,56 2200 2300 1 1 1 1 2 1 1 1 1 1,11 2300 2400 2 1 2 1 2 1 2 2 2 1,67 2400 2500 2 2 1 2 2 1 2 2 2 1,78 2500 2600 2 2 2 2 2 1 2 1 2 1,78 2600 2700 2 2 2 2 3 2 2 2 2 2,11 2700 2800 3 2 1 2 3 2 2 2 2 2,11 2800 2900 2 2 1 2 3 2 2 2 2 2,00 2900 3000 2 1 1 1 2 1 1 1 1 1,22 3000 3100 1 1 1 1 2 1 1 1 1 1,11 3100 3200 1 1 1 2 2 1 1 1 1 1,22 3200 3300 1 1 1 2 1 1 1 1 1 1,11 3300 3400 1 1 1 1 1 1 1 1 1 1,00 3400 3500 1 1 1 1 1 1 1 1 1 1,00

12 3

Why assets management?M

inim

um

acc

epta

ble

GO

OD

New Road

Time / Years

Traffic wear

It is very expensive with poor roads!Accidents, Car damages, Travel time, Fuel consumption…

But if i wait to long…it is very very expensive

Road maintenancecost money

IRI

IRI – Information Quality Levels (IQL)

• IRI measured with various profilometric methods [1]– Class 1 - Precision profiles (laser – very accurate)– Class 2 - Other profilometric methods (direct computation)– Class 3 - IRI estimates from correlation equations response type– Class 4 - Subjective ratings and uncalibrated measures (visual)

eIRI (estimated IRI) – Class 3

• Our first model for three type of vehicle bodys– small car/business van– medium/big sedan/station wagon– 4WD jeep

• Graph functions used to make eIRI speed compensation.

0

0,2

0,4

0,6

0,8

1

1,2

20 40 60 80

g

Averaged speed dependent response in g:s per km/h and vehicle compared

Scenic large bump

Scenic small bump

Hilux large bump

Hilux small bump

Kangoo large bump

Kangoo small bump

eIRI vs. cIRI (calculated IRI)

• eIRI – IQL 3/4– Using eIRI needs a linear conversion formula – Extensive IRI correlation studies to obtain the formula– Data collection speed paved roads - 20 – 100 km/h– Research by independent universities has found that eIRI have a 81%

correlation with IRI laser measurement systems [3][4]– eIRI cant be much more accurate, so our R&D focused on cIRI.– eIRI is sensitive for sudden impacts and surface/micro roughness

• cIRI – aim for IQL 2/3– use the QCS (quarter-car system) IRI algorithm [1]– have a vehicle sensitivity adjustment– need a consistent speed between 60 - 90 km/h to work correctly– cIRI calculates IRI for a given section length and is less sensitive for

sudden impacts and micro surface.

IQL and Level of Detail [5]

Data analyzed in 100 Hz andsaved every second with a GPS-coordinate

Visualizing road condition

• Roughness data as ”dots” each second – or matched to road links.• App use the camera to take GPS-tagged photos for display on map

Quick analysis by polygons• By drawing an arbitrary shape to filter it is possible to do quick

roughness calculations for specific areas• Road condition data can be exported in GIS/Shape-file format

1.200.000 points - Myanmar

Monitor Rougness changes over time

• Easy to continuously collect data, daily, weekly • Compare % of classes to study changes over time.

Road Condition Change report Q4 - 2012Gävleborg

Hudiksvall Contractor 69,4% 15,5% 7,4% 7,8% 65,8% 14,6% 8,5% 11,0%

1089 Km Phone 010-476 14 07 Q4 - 2012 Helår - 2012

Road no. Traffic Class Length Comments Good Sat Usat Poor TREND Good Sat Usat Poor eIRI avg

E4 14000 1 143 93,9% 4,6% 0,9% 0,5% -3,4% 97,4% 2,0% 0,4% 0,3% 1,8

83 8300 2 167 Salt road 88,9% 7,4% 2,2% 1,5% 3,3% 85,6% 8,0% 3,2% 3,2% 2,6

84 7500 2 210 Salt road 90,9% 6,1% 1,7% 1,3% -1,6% 92,5% 4,8% 1,6% 1,1% 2,9

305 1200 3 105 76,7% 14,4% 5,3% 3,6% -0,6% 77,3% 13,3% 5,2% 4,1% 4,5

307 900 3 75 93,7% 5,2% 0,7% 0,4% 0,4% 93,3% 5,5% 0,8% 0,4% 3,7

539 300 3 33 Gravel road 9,1% 23,2% 24,2% 43,4% 7,5

583 1700 3 89 96,9% 2,6% 0,2% 0,3% 0,0% 96,9% 2,0% 0,6% 0,5% 2,3

660 1850 3 64 88,6% 8,3% 0,6% 2,5% 9,1% 79,5% 9,7% 4,5% 6,3% 6,7

Good for Q4 minus Good for all year.

• % of 4 IRI classes for a specific road section in spring

Daily data with a

post car.

What happens atTerremotos?Tsumanis?

How bad – where…

Monitor Roughness changes over time

GPS HD Video

Support for visual inventorys

Input of Rutting, Cracks, ectInput and GPS-coordinate saved each second

Input by smartphonesInventario VIAL

Download directly:IRI, Speed, Grade in 20-200 m sections

Exports to your system:HDM4, RMMS, PMS, SIEM, SAMR, SPVG

GIS/Shape, ex: SIGVial

App to Measure:IRISpeedPhotos

Spatial data/Shape:Longitud/LatitudeAltitude (Grade%)

App forVisual road inventorys:RuttingEdgebreakes, DrainageRailings etc

Research and training for universities!

References

• [1] Michael W. Sayers, Thomas D. Gillespie, and Cesar A. V. Queiroz, “The International Road Roughness Experiment: Establishing Correlation and a Calibration Standard for measurements,” World Bank Technical paper number 45, Washington DC, 1986.

• [2] K.E.Tarr, “Evaluation of Response Type Application for Measuring Road Roughness”, University of Pretoria, South Africa, 2013

• [3] Myles Johnston. “Using cell-phones to monitor road roughness”, University of Auckland, Auckland, New Zealand, 2013

• [4] Tasnimul Islam. “Using cell-phones to monitor road roughness” , University of Auckland, Auckland, New Zealand, 2013

• [5] C. Bennet. 2013_wb_trends_road_asset_management, The world bank• [6] M R Schlotjes, A Visser, C Bennet. Evaluation of a smart phone

roughness meter, University of Pretoria

Conclusions!

eIRI IQL 3 [5] – cIRI more accurate.

Low volume roads

• Cost efficient - No specific hardware or cars

• Durable – and no rare or expensive spare parts

• Portable – bring anywhere

• Easy to operate

High-end roads

• Support planning of IQL1 surveys

• Continuous data collection

– Road patrols, post cars or crowd.

– Monitor changes over time

– Get early warnings, optimize

• Exports to RMMS/HDM4

– 20, 50, 100, 160 or 200 m sections

lars.forslof@roadroid.com – add me on linked in or join us on twitter