roadroid - 4 congreso regional irf latin, v6
DESCRIPTION
Our presentation in the 4:th IRF Latin Congress in Lima, Peru. September 2014.TRANSCRIPT
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Road condition surveys using smartphonesMonitoreando la condición de camino con el uso de
teléfonos inteligentesLars Forslof - CEO/founder - Road engineer
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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
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Smartphone -> IRI -> RMMS
www Exports: IRI in 20 -200 mHDM4, RMMS, PMS, SIEM
GIS/Shape, ex: SIGVial
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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
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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
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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)
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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
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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.
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IQL and Level of Detail [5]
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Data analyzed in 100 Hz andsaved every second with a GPS-coordinate
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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
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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
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1.200.000 points - Myanmar
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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.
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• % 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
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GPS HD Video
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Support for visual inventorys
Input of Rutting, Cracks, ectInput and GPS-coordinate saved each second
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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
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Research and training for universities!
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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
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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
[email protected] – add me on linked in or join us on twitter