SARF/IRF 2014 | 2-4 September, South Africa

Roadroid - continuous road condition

monitoring with smartphones

The Roadroid team

• Who are we?

– Lars Forslof - CEO/founder, road engineer/ITS

– Hans Jones (me) – mobile developer, server/security

– Tommy Niittula - web/GIS/database developer

– Martin Snygg - support/GIS/social media

• We all have been working with mobile ITS since

the mid-90s. Particularly with mobile devices,

GPS, road surveys, road weather and road

databases.

Roadroid history

1. 2003-2004 PC / GPS / external accelerometers, MATLAB

2. 2004-2006 ”Car PC”, Win98, external accelerometers, C++

3. 2010- Smartphone/app revolution, all that’s needed 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

1

2 3

Why collect and monitor

road condition data continuously?

Go

od

S

ati

sfa

cto

ry

Po

or

Un

sati

sfa

cto

ry

Time

New road Well maintenaded roads Decreases road and vehicle operating/maintenance costs. Increase traffic safety, ride comfort, …

Traffic/weather/wear

Worn out road

Poor roads Vehicle damage, accidents, travel time, logistics, fuel consumption, …

Waiting to long ...

Very expensive!

Road maintenance = money

Limit is road class dependable

IRI (International Roughness Index) and

RRMS (Road roughness measuring system)

Go

od

IRI < 2.5

IRI 2.5 - 4

IRI > 6

• Road roughness is measured with various profilometric methods [1] – Class 1 - Precision profiles (laser – validation)

– Class 2 - Other profilometric methods (also a direct IRI computation – less accurate)

– Class 3 - IRI estimates from correlation equations

– Class 4 - Subjective ratings and uncalibrated measures (visual)

Po

or

Sa

tisf

act

ory

IRI 4 - 6

Un

sati

sfa

cto

ry

m/km

Class 3 is

usually a

response-

type RRMS

(RTRRMS )

Potential problems

• Vehicle chassis – Mounting is inside - different vehicle chassis give different signals

– A stable mounting bracket is required

• Sufficient accelerometer performance – Usually have a +/- 2 g resolution, but +/- 4 g exists as well

– Low max sample rate, usually between 80 – 120 Hz • 90 km/h (25 m/s) / 100 Hz = 250 mm –> interval is within the IRI accuracy req.

• Max freq. rate cannot be controlled hard - may fluctuate

• The effect of different smartphone devices and OS versions – Accelerometer hardware and firmware calibration

– GPS accuracy

• Other variables influence (common to other systems as well) – Speed, road path, tyre size, type and pressure, temperature, vehicle load

and shocks/springs, driver etc.

• Other unknown effects ...

Example of raw sample response and

collected number of samples per second

-1.5

-1

-0.5

0

0.5

1

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

11

3

12

1

12

9

13

7

14

5

15

3

16

1

16

9

17

7

18

5

19

3

20

1

20

9

21

7

22

5

23

3

24

1

24

9

25

7

26

5

27

3

28

1

28

9

29

7g

Raw samples - 80km/h test run over large and small bumps

Sample rate

fluctuation during

2600 seconds

Device calibration

• Device (phone) calibration needed to find the accelerometer sensitivity

0

2

4

6

8

1 8

15

22

29

36

43

50

57

64

71

78

85

92

99

10

6

11

3

12

0

12

7

13

4

14

1

14

8

15

5

16

2

16

9

17

6

18

3

19

0

19

7

20

4

21

1

21

8

22

5

23

2

23

9

24

6

25

3

26

0

26

7

27

4

28

1

28

8

29

5

30

2

m/s

²

STD of raw samples when device have been put in a "shaker"

• Samsung GT-P1000 with Android v2.2.x is the reference device

0

0.5

1

1.5

Samsung GT-

P1000 2.2.x

Samsung GT-

P1000 2.3.x

Samsung GT-

I9100 2.3.x

Samsung GT-

I9100 4.x

Samsung GT-

N7000 2.3.x

Samsung GT-

I930x 4.x

Samsung GT-

N7100 4.x

Samsung GT-

I9105P 4.x

LGE Nexus 4

4.x

Huawei Y300

4.x

Calibrated sensitivity adjustment constant

low value = less sensitive

estimated IRI (eIRI)

• Our first generation RTRRMS model for three type of vehicle bodies – Small car/business van (Kangoo)

– Medium/big sedan/station wagon (Scenic)

– 4WD jeep (Hilux)

• Graph functions used to speed compensate eIRI (activated autumn 2013 – after UP tests [2])

• It is important to know the dynamics (other variables influence) in the measurements to achieve comparable data [2]

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

km/h

eIRI vs. calculated IRI (cIRI)

• eIRI – Using eIRI needs a linear correlation equation

– Extensive IRI correlation studies to obtain the conversion formula

– Research by independent universities has found that eIRI have a 81% correlation with IRI laser measurement systems [3][4]

– eIRI can’t be much more accurate, so our R&D is focused on cIRI

– eIRI is more sensitive for sudden impacts and surface roughness

• cIRI – cIRI is a direct implementation of the QCS (quarter-car system) IRI

algorithm [1]

– cIRI needs a vehicle sensitivity adjustment value and a consistent survey 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 rough surfaces

eIRI vs. cIRI • Data is uploaded to a website and can be downloaded in aggregated 20 -

200 m sections

• Note – cIRI ”falls” with the vehicle speed whereas eIRI handles the speed change

-4

-2

0

2

4

6

8

10

12

14

16

1

33

65

97

12

9

16

1

19

3

22

5

25

7

28

9

32

1

35

3

38

5

41

7

44

9

48

1

51

3

54

5

57

7

60

9

64

1

67

3

70

5

73

7

76

9

80

1

83

3

86

5

89

7

92

9

96

1

99

3

10

25

10

57

10

89

11

21

11

53

11

85

12

17

12

49

12

81

13

13

13

45

13

77

14

09

14

41

14

73

15

05

15

37

15

69

16

01

16

33

16

65

16

97

17

29

17

61

17

93

18

25

18

57

18

89

19

21

19

53

19

85

20

17

20

49

20

81

21

13

21

45

21

77

22

09

22

41

22

73

23

05

23

37

23

69

24

01

24

33

24

65

24

97

25

29

25

61

25

93

26

25

26

57

26

89

27

21

27

53

Serie1

Serie2

Serie3

estimated IRI per 100 m

Analyze 100 samples -> 1 Value each second

Save: (X, Y, eIRI) 620029.012, 6782994.850, 4.3

Data analyzed in 100 Hz and saved

every second with a coordinate

Visualization of road condition • Road data can be viewed as ”dots” (one sample/s) – or matched to road links

• The app can use the camera to take GPS-tagged photos for display on the map

Road condition and speed maps

Example from 1.000.000

points collected in Myanmar

Quick analysis with polygons • By drawing an arbitrary shape to filter in dots, it is possible to do

quick roughness calculations for specific areas

• Road condition data can be exported in GIS/shapefile format

The Roadroid Index (RI)

• Comparing the percentage occurrence of specific dots - which allows for study of changes over time

• As it is easy to continuously collect data – it is possible to find trends

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.

RI over time • Percentage of the 4 classes for a specific road section in spring

• Data collected daily with a post office car

Information Quality Levels (IQL)[5]

Conclusions

• Improved decision making support - proven for IQL 3/4 [6] and aiming on for IQL 2

• Can handle low volume and gravel roads

• Cost efficient - no demand of specific hardware or cars

• Durable - no expensive spare parts

• Easy to operate

• Data collection can be done frequently, by road patrols, post office cars or crowd, ...

• Easy access - result is available on internet within an hour

• Exports to RMMS/HDM4 in 20, 50, 100, 160 or 200 m

• Spatial data collected and saved

• Mobile network is not needed during data collection

• Data at rest is safe - daily encrypted backup to cloud

SARF/IRF 2014 | 2-4 September, South Africa

Thank you for listening, and try keeping good roads simple!

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] Bennett, C.R. and Paterson, W.D.O. 2000. HDM-4 Volume Five: A guide to calibration and adaptation, The World Road Association (PIARC), International Study of Highway Development and Management (ISOHDM), Paris, France

• [6] M R Schlotjes, A Visser, C Bennet. ”Evaluation of a smart phone roughness meter”, University of Pretoria, South Africa, 2014

View public data at: http://roadroid.com/