Exploring the Use of Smartphone Accelerometer and Gyroscope to

Study on the Estimation of Road Surface Roughness Condition

Viengnam Douangphachanh and Hiroyuki Oneyama

Department of Civil and Environmental Engineering, Graduate School of Urban Environmental Sciences,

Tokyo Metropolitan University, 1-1 Minami-osawa, Hachioji, Tokyo, Japan

Keywords: Smartphone Sensors, Accelerometer, Gyroscope, Road Roughness Condition, Condition Estimation.

Abstract: Smartphones are potentially useful to be adopted as a cost-effective and easy to implement tool for the

measurement of road surface roughness condition, which is very essential for road monitoring and

maintenance planning. In this study, an experiment has been carried out to collect data from accelerometers

and gyroscopes on smartphones, which are placed at different locations inside vehicles running on road

sections with different roughness conditions. The collected data is processed in the frequency domain to

calculate magnitudes of the vibration. It has been revealed that at the considered frequency range of 40-

50Hz, there is a very strong relationship between road roughness condition and the magnitudes of vibration,

calculated from each axis of the accelerometers and gyroscopes; as well as the average speed. Road

roughness condition that is modelled as a linear function of the vibration magnitudes, taking into account of

both data from accelerometer and gyroscope as well as the average speed, achieves better estimation than

the model that takes into account the magnitude from the accelerometer and the average speed alone. The

finding is potentially significant for the development of a more accurate model and a better smartphone app

to estimate road roughness condition from smartphone sensors.

1 INTRODUCTION

To properly monitor, plan for maintenance and

manage road infrastructure, substantial amount of

data is always needed, particularly time series and

up to date road condition data. Road condition data

changes over time; since it also usually requires

considerably significant investment and time to

collect the data on a regular basis, obtaining such

data is often a challenge that many governments are

facing, especially in countries where budget is

limited and advance technology is still unaffordable.

Road surface roughness is regarded as one of the

most important road conditions, because it affects

vehicle maintenance costs, fuel consumption,

comfort, and safety. International Roughness Index

(IRI) is an indicator that is widely adopted as a

measurement for road surface roughness condition.

IRI measurement is normally done either by one or a

combination of two main approaches, which include

a subjective rating or a visual inspection, an

approach that is labour intensive and very time

consuming; and the use of sophisticated profilers,

which are highly accurate but costly to obtain,

operate and maintain, requires skilful operators as

well as cumbersome calibration before deployment.

In the smartphone era, where the number of

smartphone users is increasing steadily, using

smartphones to collect road condition data and

estimate road roughness condition could change the

way the government monitor, plan for maintenance

and manage the road infrastructure forever, because

the chance of having plenty of up to date data with

inexpensive investment is huge. On the other hand,

today’s smartphones usually come with sensors that

are capable of recording useful signal for road

surface condition estimation similarly to those used

in many high-tech equipment.

There are some studies that are relevant to this

work, such as the use standalone, mobile and

smartphone sensors to assess and monitor road and

traffic conditions, detect road bumps/anomalies and

their locations, and analyse events/features of

different road defects; in simulation and real-life

traffic conditions (Gonzalez, et al., 2008; Eriksson,

et al., 2008; Mohan, et al., 2008; Tai, et al., 2010;

Menis, et al., 2011; Strazdins, et al., 2011; Perttunen,

et al., 2011). Further development includes the

783

Douangphachanh V. and Oneyama H..

Exploring the Use of Smartphone Accelerometer and Gyroscope to Study on the Estimation of Road Surface Roughness Condition.

DOI: 10.5220/0005117407830787

In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2014), pages 783-787

ISBN: 978-989-758-039-0

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

introduction of smartphone apps that claim to work

in detecting road bumps (BumpRecorder, 2013) and

roughness condition (Roadroid, 2013). The final

goal of this study, however, is to develop a

significantly simpler app that does not require the

smartphones to be fixed at a specific location, in a

specific orientation, and a troublesome calibration

before use.

Our previous studies (Douangphachanh and

Oneyama, 2013a; 2013b; 2013c) suggest that there

is a strong relationship between the magnitudes of

the vibration from smartphone accelerometers, IRI,

and the average speed. The strength of the

relationship also differs at different frequency

ranges, in which the strongest relationship is

observed at the frequency range of 40-50Hz. Based

on these findings, IRI is roughly modelled as a linear

function of the magnitudes, from accelerometers,

and the average speed.

The main objective of this study is to explore

whether adding gyroscope vibration magnitudes, in

the linear function, would improve the estimation

results, in comparison to the function that only takes

into account the magnitudes from accelerometers

and the average speed, which is included in the

scope of our previous studies. This study only

focuses at the frequency range of 40-50 Hz.

2 APPROACH

2.1 The Experiments

To achieve the objective of this study, we use the

data from the experiment that is conducted in

Vientiane, Laos in November, 2012. In the

experiment, many smartphones and equipment are

set at different location inside experiment vehicles

that run along selected roads to collect data for our

analysis.

Main equipment used in this experiment includes

4 smartphones, 2 Samsung Galaxy Note 3 (GT-

N7100), a Galaxy S3 (GT-I9300), and a LG 4X HD

(LG-P880); a GPS trip recorder (747Pro), and a

Sony video camera.

The smartphones are pre-installed with an

application called AndroSensor (2012). The

application is used to record data from

accelerometers, gyroscopes and GPS. Data recording

is done at an interval of 0.01 second or at a sampling

rate of 100Hz. This sampling rate enables us to

study the magnitudes of the vibration from the

frequency range of 0-50Hz.

The roads selected for the experiment include

various sections with diverse roughness conditions

ranging from good (0≤IRI<4), fair (4≤IRI<7), Poor

(7≤IRI<10), and bad (IRI≥10). These condition

classifications are based on condition indices used in

Lao Road Management System.

We use Vehicular Intelligent Monitoring System

(VIMS, 2012a) to measure the actual roughness

condition of the selected road sections. VIMS

comprises of both hardware, which includes a data

acquisition module, an accelerometer and a GPS

logger, all connected to a laptop computer via

cables; and software, which includes two main

programs, one for calibration and data collection,

and another one for the analysis. The system

calculates the International Roughness Index (IRI)

for every 10 meter road section. The main limitation

of VIMS is that it cannot estimate IRI of road

sections where the travel speed of the experiment

vehicle is less than 20km/h.

In our experiment setting, we place the

smartphones at 3 different locations, on the

dashboard, inside the driver’s shirt pocket and in a

box near the gearshift (Figure 1). On the dashboard,

two smartphones are glued tightly assuming that the

orientation of the smartphones is fixed (Smartphone

A and Smartphone B), while the other two

smartphones inside the pocket and the box are

allowed to move freely (Smartphone C and

Smartphone D, respectively). It is important to note

that Smartphone C, though can move accordingly

with the driver’s movement, will not change its

orientation. Smartphone D, which is inside the box

near the gearshift, however, may change its

orientation if the vehicles shake violently due to

severe road surface condition. The violent vehicle

shaking does not happen during the course of this

experiment.

Figure 1: The experiment setting.

ICINCO2014-11thInternationalConferenceonInformaticsinControl,AutomationandRobotics

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Figure 1 also shows the setting of the GPS and the

video camera, which are also placed on the dash

board. VIMS components are also installed in

accordance to VIMS (2012b).

Four vehicles are used in this experiment, a

Toyota Vigo 4WD pick-up truck as Vehicle 1,

Vehicle 2 is a Toyota Camry sedan, Vehicle 3 is a

Toyota Vigo 2WD pick-up truck, and Vehicle 4 a

Toyota Yaris sedan.

Note that throughout the experiment, the two

Samsung Galaxy Note 3 have been assigned as

Smartphone A and C, the Galaxy S3 has been

assigned as Smartphone B, and the LG 4X HD has

been assigned as Smartphone D. For the Vehicle 1,

Smartphone C and D have been switched their

locations on one occasion during the entire course of

the experiment. See Table 1 for details.

2.2 Data Processing and Analysis

Data processing for this study is similar to those

described in our previous studies. The following

figure (Figure 2) shows the data processing

flowchart:

Figure 2: Data processing and analysis flowchart.

After data is obtained, it is checked and filtered

(high pass) to remove irrelevant data and signal.

Next, the qualified data will be matched with

roughness data before dividing into 100 meter

sections. A 100 meter length of sensors’ data is

chosen as a unit for road roughness estimation in this

study because (i) Road Management System in Laos

requires road pavement condition to be estimated for

every 100 meter section as it is believed to be

convenient for maintenance planning; (ii) there is

also a concern on the accuracy of GPS position data,

therefore choosing a shorter section unit may cause

some issues in data matching between VIMS and

smartphone GPS data.

Also similar to the previous studies, after

sectioning, road sections that have incomplete data

will be excluded from the analysis. The sections

with incomplete data are those that have no data

from VIMS, at the time when the experiment vehicle

is travelling at a speed slower than that required by

VIMS (less than 20km/h) in traffic jam condition,

for instance; and sections that have no GPS data, as

sometimes GPS would fail to record information due

to some satellite signal obstruction. Road sections

where experiment vehicles have stopped (checking

from speed and VIMS data) are also excluded since

data at these sections cannot be used to estimate road

roughness condition. In addition, sections that have

the lengths that are 10% less or more than 100

meters, less than 90 meters or more than 110 meters,

are also omitted from the analysis.

Table 1: Number of road sections by smartphone,

locations, and vehicles.

The analysis is carried out in the frequency

domain using Fast Fourier Transform (FFT) to

calculate magnitudes for every selected 100 meter

section for each axis of the acceleration and

gyroscope vibration. For the sampling rate of 100

Hz, FFT can calculate the magnitudes for each

vibration axis from 0-50 Hz. FFT results allows us

to study the relationship between the magnitudes and

IRI at different ranges of frequency, to see whether

the sum of magnitudes at a particular range of

frequency is more useful in expressing the road

roughness condition or not.

3 RESULTS AND DISCUSSION

The analysis shows that IRI can also be roughly

modelled as a linear function of the magnitudes,

calculated from both accelerometer and gyroscope

data, and the average speed. By adding gyroscope

vibration as an additional parameter in the function,

significant improvement in the estimation of IRI is

observed.

Figure 3 below shows a selected result of a

comparison of IRI estimation for the function that is

taking into account only the average speed and the

magnitudes, from accelerometer (Function 1); and

ABCaCbDaDbABCDABCDABCD

Number of

sections selected

for analysis

703 674 311 246 320 492 497 489 467 592 314 319 309 421 408 411 382 450

Location of

smartphone

Dashboard

Pocket

Near gearshift

Pocket

Dashboard

Pocket

Near gearshift

Dashboard

Pocket

Near gearshift

Dashboard

Pocket

Near gearshift

Vehicl e 1 : Toyota VIGO

4WD Pick UP

Vehicle 2: Toyota

Camry Sedan

Vehicle 3: Toyota

VIGO 2WD Pick

Vehicl e 4 : Toyota

Yaris Sedan

Smartphone Smartphone Smartphone Smartphone

ExploringtheUseofSmartphoneAccelerometerandGyroscopetoStudyontheEstimationofRoadSurfaceRoughness

Condition

785

the function that considers the average speed and the

magnitudes from both accelerometers and

gyroscopes (Function 2).

Smartphone A

Smartphone B

Smartphone C

Smartphone D

Note: Graphs on the left hand side are the

estimated by Function 1; while graphs on the right

hand side are estimated using Function 2.

The

frequency range is

40-50Hz. AvgIRI is the

average IRI.

Figure 3: Comparison of estimation results, for all

smartphones in Vehicle 3.

As the figure shows, for all smartphones in

vehicle 3, the R

values in the right hand side graphs

are greater than the R

values in the left hand side

graphs. This indicates that Function 2 is better than

Function 1. Smartphone B, for instance, the R

improves significantly from 0.575 to 0.766 of the

estimation predicted by Function 1 and Function 2,

respectively.

Table 2: Summary of estimation results for both functions,

all smartphones and all vehicles.

Table 2 above summarises R

that are derived

from the estimation of Function 1 and Function 2 for

all smartphones and all vehicles. In general, it can be

concluded that R

that are estimated by Function 2

are greater than that estimated by Function 1.

Almost all R

estimated by Function 2 are greater

than 0.74, while less than half of R

estimated by

Function 1 reach that value. The greatest values of

from Function 2 are as great as 0.8 in four cases

(0.804, 0.806, 0.809, and 0.817 for Smartphone C

Vehicle 4, Smartphone Da Vehicle 1, Smartphone

Ca Vehicle 1, and Smartphone A Vehicle 4,

respectively). In many cases, Smartphone B Vehicle

3, Smartphone D Vehicle 2, and Smartphone B

Vehicle 1, for instances, there are significant

differences between R

values estimated by the two

R² = 0,658

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

R² = 0,790

0481216

Actual AvgIRI

Predicted AvgIRI

R² = 0,575

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

R² = 0,766

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

R² = 0,696

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

R² = 0,792

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

R² = 0,755

0481216

Actual AvgIRI

Predicted AvgIRI

R² = 0,788

0 4 8 12 16

Actual AvgIRI

Predicted AvgIRI

Accelerometer

(Function 1)

Accelerometer

and Gyroscope

(Function 2)

A 0.735 0.817

B 0.666 0.735

C 0.790 0.804

D 0.725 0.739

A 0.658 0.790

B 0.575 0.766

C 0.696 0.793

D 0.755 0.788

A 0.616 0.764

B 0.620 0.758

C 0.550 0.694

D 0.520 0.736

A 0.600 0.793

B 0.545 0.736

Ca 0.775 0.809

Cb 0.602 0.618

Da 0.718 0.806

Db 0.594 0.625

Vehicle 2 Smartphone

Vehicle 1 Smartphone

derived from the estimation

function that takes intoaccount

the average speed and

magnitudes from:

Vehicle 4 Smartphone

Vehicle 3 Smartphone

ICINCO2014-11thInternationalConferenceonInformaticsinControl,AutomationandRobotics

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functions (0.575 against 0.766, 0.520 against 0.736,

and 0.545 against 0.736, respectively).

4 CONCLUSIONS

In our previous studies, we have investigated the use

of smartphone accelerometers to estimate road

surface roughness condition (IRI) in which

promising results are observed. This study continues

to explore the use of more smartphone sensors,

including the accelerometers and gyroscopes, to

estimate IRI with the final goal of obtaining an

improved estimation model that is acceptably

accurate, simple and easy to implement. An

experiment is carried out to obtain data from the

smartphone relevant sensors. After the data is

processed, FFT is used to calculate the magnitudes

of the vibration. Similar to the findings in our

previous studies, IRI can also be modelled as a

linear function of the average speed and the

magnitudes, calculated from both accelerometers

and gyroscopes. The function can be used to

estimate IRI with an improved accuracy in

comparison to the function that only considers the

average speed and the magnitudes from the

accelerometers alone, which is presented in the

previous studies. The new estimation function is

potentially useful for the development of a

smartphone app, which may contribute to improve

the efficiency of road authorities and government in

obtaining needed data, and monitoring as well as

maintenance planning of the road infrastructure.

In our ongoing studies, more focus is being put

into the formulation, piloting, and improvement of

the final and practicable estimation model.

Additionally, in our future work, great emphasis will

also be directed to the integration of the model into a

smartphone app.

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