A presentation conducted by Mr Viengnam Douangphachanh, Tokyo Metropolitan University Presented on Thursday the 3rd of October 2013. Efficient road infrastructure maintenance and management depends on many factors, of which the availability of updated pavement condition data is among the most important. Today’s smartphones, which usually come with many sensors, are potentially useful tools for pavement condition estimation. This research explores the use of data from smartphones’ accelerometers to analyze for features and relationship of acceleration vibration to estimate road roughness condition. Although, the estimation might not be as accurate as modern profilers’, it is still may be very useful for cost saving and as an indicator for continuous monitoring. In the experiment, smartphones are placed inside vehicles and drive along selected road sections to gather data for analysis. The analysis consists of data filtering, matching with location and reference data, sectioning and frequency domain analysis. Results show that acceleration vibration magnitude has a linear relationship with road roughness condition.

1. ENDORSING PARTNERS
Using smartphones
to estimate road
pavement condition
The following are confirmed contributors to the business and policy dialogue in Sydney:
•
Rick Sawers (National Australia Bank)
•
Nick Greiner (Chairman (Infrastructure NSW)
Monday, 30th September 2013: Business & policy Dialogue
Tuesday 1 October to Thursday,
Dialogue
3rd
October: Academic and Policy
Presented by: Mr Viengnam Douangphachanh, Tokyo Metropolitan
University
www.isngi.org
www.isngi.org

2. Using Smartphones to Estimate
Road Pavement Condition
Viengnam DOUANGPHACHANH
Hiroyuki ONEYAMA
16 December 2013
2

3. Using Smartphones to Estimate Road Roughness Condition
Outlines
 Introduction
 Data Processing
 Methodology  Analysis Result
 Data Collection  Conclusion and
Future Work
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16 December 2013

4. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
Good condition of
infrastructure
Challenging for
Road
governments and
road authorities
Infrastructure
Monitoring &
Substantial amount
of data needed
Maintenance
Road roughness is an
indicator for pavement
condition evaluation
Huge and increasing
number of users
Many
application in
many fields
Integrated with
many useful
sensors
Costly
Time consuming
Smart
phones
Road
roughness
condition
data
Requires sophisticated
profilers
and skillful operators
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16 December 2013
Road
roughness
condition
Safety
Vehicle
operating costs
Fuel consumption
Comfort

5. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
The final goal:
Exploring the use of smartphones for the estimation
of road pavement (roughness) condition for the
purpose of road infrastructure monitoring and
management.
Develop a smartphone application to estimate road
roughness condition, and propose a system for
continuous road condition monitoring system.
Objective of this study:
Investigate the relationship and features between
smartphone sensor data and road roughness.
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16 December 2013

6. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
Conceptual image of the system
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16 December 2013
 CONCLUSION

7. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
Assumption: different pavement conditions cause vehicles to
vibrate differently, therefore by placing smartphones that come with
acceleration sensors, the variation of the vibration is believed to be
captured.
Experiment: place smartphones with preset orientation inside
experiment vehicles, drive the vehicles on selected road sections with
different pavement condition.
Referenced data: obtain IRI of the selected road sections by using
VIMS
Analysis: calculate magnitude of acceleration in frequency domain
by performing FFT, and study the magnitudes against IRI and other
data
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16 December 2013

8. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
Experiment setting
 Data recording application:
AndroSensor
 Sensors used: Accelerometer
and GPS
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16 December 2013
 Recording rate: Every 0.01
second or 100Hz
 2 Smartphones
 4 Vehicles

9. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
Experiment arrangement and routes in
Vientiane, Laos
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16 December 2013
 CONCLUSION

10. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
 Checking
 Eliminating
incomplete data
 Filter out irrelevant
signals/noises
 Matching smartphone
and VIMS data
 Sectioning into 100
meter sections
Implementation flowchart
10 16 December 2013
 Calculate magnitudes
from acceleration
data (x,y, z) for all
100m sections
 Analysis

11. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 ANALYSIS RESULTS
 DATA PROCESSING
 CONCLUSION
20
R² = 0.7296 (Veh 3)
R² = 0.7249 (Veh 4)
Average IRI
15
R² = 0.5746 (Veh 2)
10
5
R² = 0.6314 (Veh 1)
0
0
10
20
30
40
50
Magnitude
Veh 1
Veh 2
Veh 3
Veh 4
Relationship between acceleration data from
smartphones and road roughness (IRI), Smartphone A
11 16 December 2013
60

12. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 ANALYSIS RESULTS
 DATA PROCESSING
 CONCLUSION
20
R² = 0.6064 (Veh 4)
15
Average IRI
R² = 0.6312 (Veh 2)
10
R² = 0.5816 (Veh 1)
5
R² = 0.6474 (Veh 3)
0
0
10
20
30
40
50
Magnitude
Veh 1
Veh 2
Veh 3
Veh 4
Relationship between acceleration data from
smartphones and road roughness (IRI), Smartphone B
12 16 December 2013
60

13. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 ANALYSIS RESULTS
 DATA PROCESSING
 CONCLUSION
Relationship between acceleration data from smartphones and road
roughness (IRI) at different ranges of frequency, Veh 1 Device A
0-50Hz
20-30Hz
15
R² = 0.6314
10
Average IRI
Average IRI
15
5
0
0
10
20
30
40
50
10
5
0
60
0
Sum of magnitudes
Average IRI
Average IRI
5
0
0
10
20
Sum of magnitudes
Average IRI
Average IRI
5
0
10
Sum of magnitudes
13 16 December 2013
R² = 0.573
5
15
5
10
Sum of magnitudes
15
40-50Hz
15
R² = 0.5605
5
10
0
10-20Hz
0
15
0
30
15
10
10
30-40Hz
15
R² = 0.5386
10
5
Sum of magnitudes
0-10Hz
15
R² = 0.552
R² = 0.5786
10
5
0
0
5
10
Sum of magnitudes
15

14. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
Summary of multiple regression analysis
Observations
Multiple R
R Square
Adjusted R Square
F Stat
Intercept
Magnitude
Avg. Speed
Vehicle 1
703
0.797
0.635
0.634
609.790
Coef.
-2.467
0.305
-0.013
t Stat
-5.868
28.820
-2.733
Vehicle 2
497
0.759
0.577
0.575
336.571
Coef.
-6.476
0.498
-0.010
t Stat
-8.756
20.757
-1.578
Vehicle 3
314
0.855
0.731
0.729
421.594
Coef.
-3.484
0.311
-0.007
t Stat
-5.893
23.603
-1.048
Vehicle 4
408
0.852
0.726
0.725
537.113
Coef.
-5.651
0.409
0.008
t Stat
-9.096
24.678
1.391
Device B
Observations
Multiple R
R Square
Adjusted R Square
F Stat
Intercept
Magnitude
Avg. Speed
14 16 December 2013
Vehicle 1
674
0.774
0.599
0.598
501.448
Coef.
-2.423
0.341
-0.027
t Stat
-4.929
24.595
-5.415
Vehicle 2
489
0.798
0.638
0.636
427.417
Coef.
t Stat
-4.317 -7.604
0.403 23.684
-0.016 -2.905
Vehicle 3
319
0.805
0.647
0.645
290.138
Coef.
-5.348
0.383
0.001
t Stat
-6.918
19.531
0.106
Vehicle 4
411
0.779
0.607
0.605
314.653
Coef.
-3.482
0.352
-0.003
t Stat
-4.835
17.879
-0.528
Predicted sum of magnitudes
Device A
50
40
30
20
10
0
0
10
20
30
40
50
Observed sum of magnitudes
Observed and
predicted sum of
magnitudes by
multiple regression
model (Vehicle 1,
Device A)

15. Using Smartphones to Estimate Road Roughness Condition
 METHODOLOGY
Veh 1
Device A
Average IRI
0≤IRI<4
4≤IRI<7
7≤IRI<10
IRI≥10
Sum of magnitudes
Index
Good
Fair
Poor
Bad
 DATA COLLECTION
Good
15 16 December 2013
Fair
Poor
Condition Index
Fair
Poor
Condition Index
60
55
50
45
40
35
30
25
20
15
Bad
 CONCLUSION
Bad
Veh 1
Device B
Good
Sum of magnitudes
Sum of magnitudes
60
55
50
45
40
35
30
25
20
15
 ANALYSIS RESULTS
Classification of magnitude by
condition index
60
55
50
45
40
35
30
25
20
15
Good
Veh 2
Device A
 DATA PROCESSING
Sum of magnitudes
 INTRODUCTION
60
55
50
45
40
35
30
25
20
15
Fair
Poor
Condition index
Bad
Veh 2
Device B
Good
Fair
Poor
Condition index
Bad

16. Using Smartphones to Estimate Road Roughness Condition
 INTRODUCTION
 METHODOLOGY
 DATA COLLECTION
 DATA PROCESSING
 ANALYSIS RESULTS
 CONCLUSION
1. Acceleration data from smartphones has linear
relationship with road roughness condition. The
relationship also partly depends on speed, vehicles
and devices.
2. There is no significant difference of the relationship
at different ranges of frequency.
3. Based on the condition indices, similar tendency of
the classification of the sum of magnitudes of
acceleration vibration is observed.
4. A simple linear model can be adopted to estimate
IRI roughly, which is good enough for maintenance
planning and continuous monitoring purposes.
16 16 December 2013

17. Using Smartphones to Estimate Road Roughness Condition
KEY ONGOING AND FUTURE WORK
1. Consider realistic settings of the smartphones
and analyze different frequency ranges of the
magnitudes
2. Formulate a simple model to estimate road
roughness
3. An application development
4. Participatory data collection trial.
Thank you very much for your
attention
17 16 December 2013