Roadroid is a system that uses smartphones to continuously monitor road conditions by collecting road roughness data through the built-in vibration sensors in smartphones. This allows road organizations to more frequently and cost-effectively collect road condition data, which enables early warnings of changes or damage and more accurate operational road maintenance management. The collected measurement data is wirelessly transferred to an online mapping server where it can be aggregated, exported, and used by other road management systems. Roadroid provides an efficient, scalable, and cost-effective way for road organizations to monitor and report on road conditions.
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1. Journal of Civil Engineering and Architecture 9 (2015) 485-496
doi: 10.17265/1934-7359/2015.04.012
Roadroid: Continuous Road Condition Monitoring with
Smart Phones
Lars Forslöf1
and Hans Jones1, 2
1. Roadroid AB, Ljusdal 82735, Sweden
2. Department of Computer Engineering, Dalarna University, Borlänge 78450, Sweden
Abstract: Road condition is an important variable to measure in order to decrease road and vehicle operating/maintenance costs, but
also to increase ride comfort and traffic safety. By using the built-in vibration sensor in smart phones, it is possible to collect road
roughness data which can be an indicator of road condition up to a level of Class 2 or 3 in a simple and cost efficient way. Since data
collection therefore is possible to be done more frequently, one can better monitor roughness changes over time. The continuous data
collection can also give early warnings of changes and damage, enable new ways to work in the operational road maintenance
management, and can serve as a guide for more accurate surveys for strategic asset management and pavement planning. Collected
measurement data are wirelessly transferred by the operator when needed via a web service to an internet mapping server with spatial
filtering functions. The measured data can be aggregated in preferred sections, as well as exported to other GIS (geographical
information systems) or road management systems. Our conclusion is that measuring roads with smart phones can provide an efficient,
scalable, and cost-effective way for road organizations to deliver road condition data.
Key words: Road condition, asset management, mobility, smart phone, intelligent transportation systems.
1. Introduction
The IRI (international roughness index) [1] is a road
roughness index commonly obtained from measured
longitudinal road profiles. Since its introduction in
1986, the IRI standard has become commonly used
worldwide for evaluating and managing road systems
[2]. However, measuring road roughness has been used
since early 1900s for expressing road condition and
ride quality [3]. Since the end of the 1960s, however,
most road profiling is done with high speed road
profiling instruments [2].
The modern traditional techniques for measuring
roughness may be categorized as either specially built
trucks or wagons with laser scanners, bump-wagons or
even manually operated rolling straight edges.
Specially built measurement equipment is expensive,
due to heavy and complex hardware, low volume of
Corresponding author: Lars Forslöf, B.Sc., CEO-founder,
research fields: intelligent transportation system, mobile
surveying and traffic management. E-mail:
lars.forslof@roadroid.com.
production and the need of sophisticated systems and
accessories.
Data gathering and analysis are often time
consuming. In the northern hemisphere, data collection
is typically done during the summer, then analyzed
and delivered to the maintenance management systems
in late autumn. During the winter and spring, the road
usually faces a continual frost heave/thaw (a very
dramatic period in a road’s life with extreme changes in
roughness) cycle. The IRI values that were “exact”
almost a year ago may now not be the same any longer.
Besides, since it is so expensive to collect and analyze
the data, many roads are only covered in one lane
direction every 3rd or 4th year.
Smart phone based gathering of roughness data
which essentially are a RTRRMS (response-type road
roughness measuring system) [1] can be done at a low
cost and monitor changes on a daily basis. For frost and
heave issues, it can tell when and where it is happening
and if the situation is worse than in previous years. It
can also be used in the winter to determine the
DDAVID PUBLISHING
2. Roadroid: Continuous Road Condition Monitoring with Smart Phones486
performance of snow-removal and ice-grading. It may
be advantageously used in performance based
contracts or research on road deterioration, various
environmental effects (as heavy rains, flooding, etc.)
and other adjacent purposes.
It should be mentioned that smart phone based
systems like Roadroid might challenge old road
knowledge with regard to standards, procedures and
existing ways to procure:
pavement planners and road engineers knowledge
of existing solutions and inputs;
research organizations, suppliers and buyers who
have established existing ways to work;
organizations which have invested time, prestige
and huge amounts of money to develop complex data
collection and management systems which can present
a very exact result.
As described in Ref. [1], it is necessary to
understand the difference between the four generic
classes of road roughness measuring methods in use:
Class 1—precision profiles;
Class 2—other profilometric methods;
Class 3—IRI estimates from correlation
equations;
Class 4—subjective ratings and uncalibrated
measures.
Data collection with smart phones will not directly
compete with Class 1 [1] precision profiles
measurements, but instead, complement them in a
powerful way. As Class 1 data are very expensive to
collect, it cannot be done often. Beside this, advanced
data collection systems also demand complex data
analysis and take long time to deliver the result. With
smart phone based data collection, it is possible to meet
both these challenges. A smart phone based system is
also an alternative to Class 4—subjective rating [1], on
roads where heavy, complex and expensive equipment
is impossible to use, and for bicycle roads. The
technology is objective, highly portable, and is simple
to use. This gives a powerful support to road
inventories, inception reports, tactical planning,
program analysis and support maintenance project
evaluation.
On the other end of the scale, many road inventories
and assessments are made by humans (Class 4
subjective ratings) over large areas using only pen and
paper. Smart phone roughness data collection fills a
gap between Class 1 measurements and Class 4 visual
inspections.
The Roadroid smart phone solution has two options
for roughness data calculation:
(1) eIRI (estimated IRI)—based on a peak and RMS
(root mean square) vibration analysis—which is
correlated to Swedish laser measurements on paved
roads. The setup is fixed but made for three types of
cars and is thought to compensate for speed between
20~100 km/h. eIRI is the base for the RI (Roadroid
index) classification of single points and stretches
(road links) of the road;
(2) cIRI (calculated IRI)—based on the QCS
(quarter-car simulation) [1] for sampling during a
narrow speed range such as 60~80 km/h. When
measuring cIRI, the sensitivity of the device can be
calibrated by the operator to a known reference.
2. The First Prototypes 2002-2006
The Roadroid team has been working with mobile
ITS since the mid-1990s, particularly with mobile data
gathering, road weather information and road
databases. During a visit to the Transportation
Research Board in Washington in 2001, a Canadian
project was presented that monitored the speed of
timber hauling trucks. It simply assumed that if the
speed was low, the road quality was poor. The idea was
to add vibration measurements.
Together with the Royal Institute of Technology, a
first pilot scheme was built in 2002-2003. At that time,
we used a high-resolution accelerometer at the rear axle
of a front wheel drive vehicle, connected by cable to a
portable PC (personal computer) through a signal
conditioner. Two master students built a first prototype
using an industrial software system for signal analysis.
3. Roadroid: Continuous Road Condition Monitoring with Smart Phones 487
The initial results were promising and the SNRA
(Swedish National Road Administration) financed a
R&D (research and development) project to further
develop and validate the prototype with a focus on
gravel roads. The system was developed for an
embedded Windows car PC with external GPS (global
positioning system) and GSM (global system for
mobile communications) capabilities as well as a
special A/D (analogue/digital) board connected to the
accelerometer. Also, a client and web based GIS
(geographical information systems) tool was
implemented for viewing the road quality spatially in
different colors.
A validation between visual inspections and the
systems measurements was performed and presented at
the transport forum in Linköping in 2005.
This research was based on 35 segments of 100 m
which were individually assessed according to four
road condition classes. A MATLAB module analysis
(experimental analysis of oscillation) was performed
on reference samples of specific sections of the four
condition classes.
A regression analysis was then performed with rules
based on:
(1) accelerometer amplitude levels;
(2) RMS (root mean square) algorithms;
(3) measured vehicle speed;
(4) sample data length.
The analysis showed that a single test run with the
system properly could classify up to 70% correct
compared to an average of subjective visual expert
inspections. A single subjective visual expert
inspection could however vary much more from the
average correct than the systems classification. The
method was considered objective with very good
repeatability (Fig. 1).
In 2006, the development stalled. The system was
considered relatively cheap and simple to operate at the
time. In retrospect, it had several limitations,
particularly the sensor mounting and cables exposed in
the harsh often wet environment under the vehicle
chassis, also, the limitations from non-integrated
standard components as Windows 98, the car PC,
specific cable connections, and to handle the system
solution as a whole by the end user.
3. Further Development 2010-2011
In 2010, the ideas from 2002-2006 were reviewed. A
major technical development was the appearance of
smart phones. Literally, all peripherals that previously
were connected by special cables were now built into a
smart phone and the limitations of certain components
were removed by new advances in technology. We
knew the answers to some of the questions from
2002-2006, e.g., the basis for signal analysis, the
influence of speed and different vehicle characteristics,
etc.. There were however new big questions to solve,
such as:
(a) (b)
Fig. 1 Images from the history: (a) 1st prototype 2002-2003; (b) 2nd prototype, developed from 2004-2006.
Source: Roadroid internal documents.
rån 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
000 1100 1 1 1 2 1 1 1 1 2 1,22
100 1200 1 1 1 2 1 1 1 1 1 1,11
200 1300 1 2 2 1 1 1 1 1 2 1,33
300 1400 2 2 3 2 3 2 3 2 3 2,44
400 1500 2 2 2 3 4 3 2 3 3 2,67
500 1600 1 1 2 1 2 1 2 1 1 1,33
600 1700 2 2 2 3 3 2 2 2 2 2,22
700 1800 2 1 1 3 3 2 1 2 2 1,89
800 1900 1 1 1 1 1 1 1 1 1 1,00
900 2000 2 1 1 1 2 1 1 1 1 1,22
000 2100 2 2 1 1 2 2 2 2 1 1,67
100 2200 2 2 1 2 2 1 1 1 2 1,56
200 2300 1 1 1 1 2 1 1 1 1 1,11
300 2400 2 1 2 1 2 1 2 2 2 1,67
400 2500 2 2 1 2 2 1 2 2 2 1,78
500 2600 2 2 2 2 2 1 2 1 2 1,78
600 2700 2 2 2 2 3 2 2 2 2 2,11
700 2800 3 2 1 2 3 2 2 2 2 2,11
800 2900 2 2 1 2 3 2 2 2 2 2,00
900 3000 2 1 1 1 2 1 1 1 1 1,22
000 3100 1 1 1 1 2 1 1 1 1 1,11
100 3200 1 1 1 2 2 1 1 1 1 1,22
200 3300 1 1 1 2 1 1 1 1 1 1,11
300 3400 1 1 1 1 1 1 1 1 1 1,00
400 3500 1 1 1 1 1 1 1 1 1 1,00
4. Roadroid: Continuous Road Condition Monitoring with Smart Phones488
Was it possible to pick up the “filtered” signals
from the vehicle chassis?
We knew different car models most certainly
would give different signals, how could we handle this?
Would a lower sampling frequency be enough
(around 100 Hz compared to earlier 512~1,024 Hz)?
Would the accelerometer sensitivity and range
(usually +/-2 g) be sufficient in a smart phone?
Would different smart phone models return
different measuring values (mainly accelerometer
sensitivity and sample frequency)?
We developed an Android application and some test
algorithms using the built-in accelerometer signal. The
choice of Android rather than iPhone was made
considering the open architecture and hardware
price/performance relation. We started to sample data
on different roads with different types of vehicles, and
run over constructed obstacles in 2011.
We choose the best hardware at the time as reference
device (Samsung Galaxy Tab GT P1000). The
obstacles were run over by different vehicle types
(from small car to large 4WD jeep) a number of times
in six different speeds: 20, 40, 60, 80, 100 and 120
km/h (Fig. 2).
Data were sampled with different devices, both with
our algorithms and by collecting the raw accelerometer
signal. During the data analysis, we discovered a
number of things:
Differences between different car models,
especially at low speeds. In the 40~80 km/h range,
differences are however limited. The tests gave us a
model for how to calculate the speed influence of the
signal for three different types or classes of vehicle
chassises;
Discrepancies between different devices, both for
the sampling frequency and the sensitivity of the
accelerometer (up to 50%). It is of great importance to
know these dynamics to achieve comparable data. A
device calibration procedure which can translate the
unit characteristics to a known reference device is
therefore required;
It is important to mount the device firmly in a
good mounting bracket, preferably in a way that
enables the devices camera lens to be directed at the
road. Unfortunately, few devices have good mounting
brackets.
Most importantly, the trials (Fig. 2) during 2011
showed that usable data could be obtained.
4. Viewing of Data
Having a device delivering measurement data (no
mobile network connection is needed during the
sampling), we needed a suitable viewer of the
information. We created a multi-user measurement
viewer—an HTML5 based map tool to present the road
condition data (Fig. 3). The data (which are encrypted)
are compressed and sent directly from the device via a
file transfer service (HTTP (hyper text transport protocol)
Fig. 2 Testing the 3rd prototype in 2011.
Source: Roadroid internal documents.
5. Roadroid: Continuous Road Condition Monitoring with Smart Phones 489
Fig. 3 A screenshot of the web GIS tool from the middle of Sweden.
Source: www.roadroid.com.
or FTP (file transfer protocol)) to our web server in the
cloud. The uploaded data files from different units are
by an hourly routine matched spatially and imported to
road links/geometries such as OSM (open street map)
[4], etc.. As background map, Google Maps with
OpenLayers [5] is used to present the road condition
data. The road condition data are divided into four
different levels for visualization: green for good;
yellow for satisfactory/OK; red for unsatisfactory/not
OK and black for poor.
The mobile app stores a number of data values each
second into a CSV (comma-separated value) file. But
to get an overview on a larger scale, it is more
convenient to use road links with aggregated and
averaged measurements than individual sampled dots.
Depending on the spatial road database, there will be
many opportunities to refine the data and add attribute
information such as road width, traffic volumes, etc.. In
Sweden, we have been using the Swedish NVDB
(national road data base) [6]. Globally, we have mostly
been using geometries from OSM. The road condition
data can be exported in shape format to other systems.
5. Use of Data and the Roadroid Index
We have been undertaking studies of the IRI and
implemented the IRI computations [7] using the QCS
for our cIRI value. According to Wakeham and
Rideout [8], there is limited benefit of using the more
complex half-car model since the half-car and
quarter-car results were nearly identical.
We have also developed a correlation between our
own road condition data algorithms and the IRI for our
eIRI value. Furthermore, we continuously have been
looking to improve the way we present the
comprehensive level of information being
collected—flexibility and scalability are the keys here.
We wanted to be able to add data from several
measurements over time and compare results over time
in a flexible way. We also wanted to automatically
generate reports for a specific road and to compare
roads with each other and to do comparisons within
whole regions. The solution was to use the percentage
6. Roadroid: Continuous Road Condition Monitoring with Smart Phones490
of each road class for the individual sampled dots
which have been spatially connected to a road link or a
geographic area (Fig. 4). We call this the RI. The RI is
scalable for a part of a road, a whole road, a city, a
region or even the whole world!
As we wanted to do continuous monitoring to view
development over time, we also needed to find a way to
produce reports. Data collection can be made by the
contractor’s road guards/officers who are doing visual
inspections one to three times per week, or by other
operators such as the local newspaper distributor. The
RI seemed to be a suitable way to also make reports
from the road condition data and trend changes over
time (Fig. 5).
6. Estimated IRI
We now had a promising and scalable index, but also
knew that we needed to correlate this to IRI. To find the
correlation, we gathered:
data from Class 1 (laser beam) IRI measurements,
both in 20 m lengths and the data averaged to road link
sections in NVDB [6];
our average road condition values, both for the
matching corresponding 20 m lengths and the whole
road link sections in the NVDB [6].
By comparing hundreds of road link sections, we
established a correlation factor and could now estimate
an IRI value (eIRI). eIRI is usable all the way from
individual sampled and classified dots with 1 Hz
resolution to a complete road link average. The
coefficient of determination (R²) was 0.5 (Fig. 6)
which meant that it is moderately correlated.
We have noticed some limitations in speed
adaptation, rough pavement surfaces and that minicars
are quite more sensitive than our reference small car.
Research is continuing by different institutions around
the world, as the World Bank, UN OPS (Office for
Project Services), specialized universities and some
large road companies as SpeaAutostrade (Fig. 7).
These organizations will report back to inform our
development department, in order to improve our
solution.
Fig. 4 How to filter, select layers and pick data out from the web tool for creating reports with the RI.
Source: www.roadroid.com.
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8. Roadroid: Continuous Road Condition Monitoring with Smart Phones492
Fig. 7 IRI comparisons of cIRI (red) vs. ARAN (autostrade Aran IRI reference, green) on selected road sub-sections.
Source: Roadroid internal documents.
(a) (b)
Fig. 8 From a 72 km test run in South Africa: (a) obtained measurement data which are sampled on a second level basis are
averaged and aggregated into 100 m chunks; (b) the resulting data are then plotted in diagrams of eIRI, speed and altitude.
Source: Roadroid internal documents.
IRI
Selected road sub-sections
Date time Latitude Longitude Distance (m) Speed (km) Altitude (m)/10 eIRI cIRI
9. Roadroid: Continuous Road Condition Monitoring with Smart Phones 493
research, cIRI has been enabled in the application.
Tests have been done which confirms that cIRI, if
calibrated correctly, can meet the demands for road
condition measurements. However, one needs to maintain
a stable speed at around 60~80 km/h for cIRI to work
correctly. The quarter-car model uses two swinging
weights which can be simulated if the vehicle chassis
movement is available as input. The estimation of the
chassis movement is based on the accelerometer data
and a vehicle calibration variable, which is adjusted
between 0.5~4, in small steps by the Roadroid operator.
Validation of sample data and the IRI output has been
made with the ProVal (Profile Viewing and Analysis)
[12] software. ProVal is the most widely-used,
validated and reliable tool for pavement profile
analysis according to the developers (The Transtec
Group) and their US clients—FHWA (Federal
Highway Administration) and the LTPP (Long Term
Pavement Performance Program). We have however
seen limited IRI-correlation on roads with rough
surfaces (as chip seals/brick roads), but at the same
time, promising results on gravel roads in Afghanistan
and Sweden. From what we have seen, it is mainly a
question of filtering the data correctly to get the correct
device motion. Further work needs to be done in this
field. As mobile technologies and sensors get more
advanced, suitable distance measuring devices for
distance monitoring between the road and chassis
might be used in the future.
8. Data Aggregator
The needs are very different—from operational
maintenance on developed road networks to first
fact finding in developing countries. Our internet
map solution is a good way to view data, but
demands some basic knowledge of GIS and road
databases.
We also developed a data aggregator which is able to
aggregate and average date/time, coordinates (latitude,
longitude), vertical profile (altitude) and speed together
with eIRI and cIRI in selectable section lengths of
25~1,000 m (Fig. 8). In the current version, CSV files
can be generated and imported into other software as
digital spreadsheets or RMMS (road maintenance
management systems).
9. Professional Use 2013-2014
Large-scale collection of measurement data in
Sweden has been taking place during a 5 month period
by the Swedish automobile association (Motormännen)
[13]. The organization will sample 92,000 km of the
Swedish road network to identify and point out road
defects. We will prepare a report to Motormännen
when the project is finished and point out where the
worst roads in Sweden are. The project is financed by
the Swedish Transport Administration (Trafikverket)
[14] which is the Swedish government agency
responsible for the long-term planning of the transport
system.
10. Use of the Built-in Camera
As most smart phones now have a high quality
built-in camera and GPS, we have developed a function
to easily take localized photos and position them on the
map (Fig. 9). The images are often of acceptable
quality, but are subject to mounting and light
conditions.
This is recognized as a very good support for visual
inspections, and can also be used to capture dynamic
events, such as certain snow conditions or other
maintenance contract issues. We have also tested a
high resolution, GPS data action video camera
(Contour+2) [15] with good results for more precise
and demanding video requirements.
11. Use on Bicycle Pathways
The cities of the world are facing more and more
traffic problems. Cycling is one option to commute
10. Roadroid: Continuous Road Condition Monitoring with Smart Phones494
Fig. 9 Picture examples of the smart phones built-in camera and support in the web tool.
Source: www.roadroid.com.
(a) (b) (c)
Fig. 10 Bike path condition monitoring: (a) Roadroid bicycle trailer mounting for bicycle pathways; (b) collected data
examples using individual sampled dots; (c) aggregated and averaged road measurements links.
Source: Roadroid internal documents and www.roadroid.com.
instead of being stuck in traffic jams. The primary need
for cyclists is safe cycle roads but also ride comfort.
One important parameter to accomplish better safety
and ride comfort is the quality of the road surface. The
11. Roadroid: Continuous Road Condition Monitoring with Smart Phones 495
same smart phone based system can be used as a
quick and easy quality monitoring system for the cycle
roads.
Since there is little or none previous work in this
field and established road condition standards as IRI
are absent, we use our own road class standard. To get
dependable and reliable results, one needs to use an
approved bicycle trailer (Fig. 10a). On the wheels axle,
one can mount the Roadroid device firmly and register
all the relevant variables. Since many cities yet have
not made any inventory of their cycle roads, one can
use the latitude/longitude data as input into a GIS tool
and create geometries for bicycle pathways
(Figs. 10b and 10c).
12. Conclusions
Measuring roads with smart phones can provide an
efficient, scalable, and cost-effective way for road
organizations to deliver road condition data. In this
paper, we have illustrated this with the use of our
software bundle—Roadroid. The system which does
not require a network connection during the data
collection can geo locate data (unmatched or matched
to existing roads) on a globally level with sufficient
accuracy. We have implemented road condition
standards based on previous work [1] as cIRI as well as
our own speed and vehicle independent eIRI standard,
which correlate up to 81% with laser measurement
systems [10, 11]. On our clouds-based web GIS
platform, one can use powerful filtering techniques as
the RI for road maintenance decision support. Further
work by examining and analyzing large data sets as
well as incorporating new sensors can further improve
the solution. By broadcasting road condition warnings
through standards for ITS, the information could
provide new kinds of dynamic and valuable input to
automotive navigation systems and digital route guides
for special traffic, etc..
Acknowledgments
The authors would like to thank the Kartographic
Society—Innovation Award, UN World Summit
Award—Global Champion in eGovernance and
European Satellite Navigation Competition—regional
winner for their help.
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