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ENDORSING PARTNERS

Using smartphones
to estimate road
pavement condition

The following are confirmed contributors to the...
Using Smartphones to Estimate
Road Pavement Condition
Viengnam DOUANGPHACHANH
Hiroyuki ONEYAMA

16 December 2013

2
Using Smartphones to Estimate Road Roughness Condition

Outlines
 Introduction
 Data Processing
 Methodology  Analysis...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 ANALYSIS RESUL...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 ANALYSIS RESUL...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 ANALYSIS RESUL...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

 METHODOLOGY

Veh 1
Device A

Average IRI
0≤IRI<4
4≤IRI<7
7≤IRI<1...
Using Smartphones to Estimate Road Roughness Condition

 INTRODUCTION

 METHODOLOGY

 DATA COLLECTION

 DATA PROCESSIN...
Using Smartphones to Estimate Road Roughness Condition

KEY ONGOING AND FUTURE WORK
1. Consider realistic settings of the ...
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SMART International Symposium for Next Generation Infrastructure: Using smartphones to estimate road pavement condition

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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.

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SMART International Symposium for Next Generation Infrastructure: Using smartphones to estimate road pavement condition

  1. 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. 2. Using Smartphones to Estimate Road Pavement Condition Viengnam DOUANGPHACHANH Hiroyuki ONEYAMA 16 December 2013 2
  3. 3. Using Smartphones to Estimate Road Roughness Condition Outlines  Introduction  Data Processing  Methodology  Analysis Result  Data Collection  Conclusion and Future Work 3 16 December 2013
  4. 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 4 16 December 2013 Road roughness condition Safety Vehicle operating costs Fuel consumption Comfort
  5. 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. 5 16 December 2013
  6. 6. Using Smartphones to Estimate Road Roughness Condition  INTRODUCTION  METHODOLOGY  DATA COLLECTION  DATA PROCESSING  ANALYSIS RESULTS Conceptual image of the system 6 16 December 2013  CONCLUSION
  7. 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 7 16 December 2013
  8. 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 8 16 December 2013  Recording rate: Every 0.01 second or 100Hz  2 Smartphones  4 Vehicles
  9. 9. Using Smartphones to Estimate Road Roughness Condition  INTRODUCTION  METHODOLOGY  DATA COLLECTION  DATA PROCESSING  ANALYSIS RESULTS Experiment arrangement and routes in Vientiane, Laos 9 16 December 2013  CONCLUSION
  10. 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. 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. 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. 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. 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. 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. 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. 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

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