A study on the use of smartphones under realistic settings to estimate road roughness condition
© Douangphachanh and Oneyama; licensee Springer. 2014
Received: 31 January 2014
Accepted: 30 June 2014
Published: 11 July 2014
Almost every today's smartphone is integrated with many useful sensors. The sensors are originally designed to make the smartphones' user interface and applications more convenient and appealing. These sensors, moreover, are potentially useful for many other applications in different fields. Using smartphone sensors to estimate road roughness condition has a great potential, since many similar sensors are already in use in many sophisticated road roughness profilers. This study explores the use of data, collected by sensors from smartphones under realistic settings, in which the smartphones are placed at more realistic locations and under realistic manner inside a moving vehicle, to evaluate its relationship with the actual road pavement roughness. An experiment has been conducted to collect data from smartphone acceleration and Global Positioning System (GPS) sensors; frequency domain analysis is also carried out. It has been revealed that the data from smartphone accelerometers has a linear relationship with road roughness condition, whereas the strength of the relationship varies at different frequency ranges. The results of this paper also confirm that smartphone sensors have a great potential to be used for estimating the current status of the road pavement condition.
Maintaining and monitoring road infrastructure is a challenging task for almost all governments and road authorities. One of the reasons is that the task requires the collection of substantial amount of road network condition data, which is very important for the maintenance planning and monitoring, over time, in addition to the significant efforts that have to be directed to actual maintenance of the road network. In developing countries, the attention that should be addressed on data collection is usually ignored or neglected mainly due to the lack of technology and budget. Therefore, in these countries, road infrastructure condition data is often left outdated, making it difficult for proper planning and programming of the maintenance.
‘Road Roughness is consistently recognized as one of the most important road condition measures throughout the world. The time series recording of roughness data allows pavement managers to assess the roughness progression rate of pavements and to take appropriate action accordingly’. International Roughness Index (IRI) is an indicator that is widely adopted to classify road roughness condition, which has been used widely for road infrastructure maintenance and monitoring for many decades. IRI is the condition index obtained from the measurement of longitudinal road profiles with the measuring unit of slope (mm/m, m/km for instance). To measure IRI, there are many approaches; however, majority of them, on the one hand, requires sophisticated profilers and tools, which are expensive to acquire and operate as well as often require skillful operators. On the other hand, visual inspection is also a popular practice in many developing countries. While this is relatively a much cheaper option to implement, it is usually very labor intensive and time consuming.
Using smartphones to collect the data is a promising alternative because of its low cost and easy to use feature in addition to its potentially wide population coverage as probe devices. In our previous study, we explored the use of smartphones, fixed to vehicles with predetermined orientation, to estimate road roughness where promising results have been observed. In order to find our new features and compare the accuracy of the estimation, this study will take a further step by attempting to estimate road roughness condition from smartphones under more realistic settings, which is beyond fixed orientation and/or fastening the devices themselves tightly with vehicles while collecting data. In other words, the smartphone are placed loosely at locations that a driver would be more likely to put their smartphones inside a car while driving.
2 Related work
There are very few studies that have directly explored the use of smartphone to estimate IRI of road pavement. In previous studies, while there is a lot of interest in detecting road bumps and anomalies using mobile sensors, majority of them focus on identifying and locating road bump and anomalies instead of estimating road pavement condition, particularly in terms of IRI measurement and/or estimation. The most relevant work to our study includes the use of a stand-alone accelerometer to fit in a simulation car and use it to assess road roughness condition. The simulations in this study conclude that roughness of the road can be estimated from acceleration data obtained from the sensor. Similarly, in another study, a system has been developed to utilize stand-alone accelerometers to successfully detect road anomalies. In India, a group of researchers use many sensing components from a mobile phone such as accelerometer, microphone, Global System for Mobile communications (GSM) radio, and Global Positioning System (GPS) to monitor road and traffic conditions. By analyzing data from the sensors, potholes, bumps, braking, and honking can be detected. The information is then used to assess road and traffic conditions. In and, Android smartphone devices with accelerometers are used to detect location of potholes. Their approach includes many simple algorithms to detect events in the acceleration vibration data. In and, the authors analyze data obtained by smartphone accelerometers in frequency domain to extract features that are corresponding to road bumps. In Japan, a group of researchers has developed an Android smartphone application called ‘BumpRecorder’ to detect the location and severity of road bumps on road networks that have been affected by the March 11, 2013, earthquake in Tohoku region, Japan.
An experiment has been conducted in Vientiane, Laos, in November 2012, to collect data for our analysis. Our initial assumption is that the vibration of vehicles may be different at different road sections depending on roughness conditions of the pavement, and by placing smartphones with relevant sensors in the vehicles, the vibration signal could be captured. With the assumption, we place smartphones and other equipment inside experiment vehicles and drive along selected roads to collect data for our analysis.
Main equipment used in this experiment includes two smartphones, a Samsung Galaxy Note 3 (GT-N7100; Samsung Electronics Co., Ltd., Suwon, Korea) and an LG 4X HD (LG-P880; LG Electronics, Seoul, Korea), a GPS trip recorder (747Pro; Transystem Inc., Hsinchu, Taiwan), and a Sony video camera (Sony Corporation, Minato, Japan).
The smartphones are pre-installed with an application called AndroSensor. The application is used to record only acceleration data (x, y, z) from accelerometer, and location data (including speed) from GPS is needed. Data recording is done at an interval of 0.01 s or at a frequency rate of 100 Hz.
The road routes selected for the experiment include various sections with different pavement 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 the Lao Road Management System.
Additionally, other equipment such as the GPS and the video camera are placed on the dashboard. VIMS components are also installed in accordance to the VIMS manual.
Vehicle and equipment
Location of smartphone
17 to 18 Nov 2012
19 to 20 Nov 2012
21 Nov 2012
22 Nov 2012
23 Nov 2012
A total of four different vehicles are used for this experiment. Vehicle 1 is a Toyota Vigo 4WD pick-up truck (Toyota Motor Corporation, Toyota, Japan), vehicle 2 is a Toyota Camry sedan, vehicle 3 is a Toyota Vigo 2WD pick-up truck, and vehicle 4 is a Toyota Yaris sedan.Note that in Figure 2, smartphone A and smartphone B are only to show the smartphone setting in our previous experiment. The smartphone setting considered under this study is therefore only smartphone C and smartphone D.
Smartphone C is a Samsung Galaxy Note 3, and smartphone D is an LG 4X HD. Vehicle 1 is chosen for data collection on two separated runs, the first run on the 17th to 18th and the second run on the 21st November 2012. On these two runs, the locations of smartphones C and D are switched.
3.2 Data processing and analysis
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 20 km/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 m, less than 90 m, or more than 110 m are also omitted from the analysis.
Road sections selected for the analysis
Number of sections selected for analysis
Location of smartphone
4 Results and discussion
4.1 Correlation between the sum of magnitudes and IRI
At the frequency range of 40 to 50 Hz, the R2 of the correlation between magnitudes and average IRI appears to be the strongest. Therefore, we believe that this frequency range is the most useful range that can be used to estimate road roughness condition (IRI) from acceleration data obtained by smartphone sensors, particularly when the smartphone is not fixed.
A further investigation into the correlations between each axis of the acceleration vibration and IRI has also been done. Similar results, as discussed above, have been observed. There is no big difference in the correlation between the magnitudes and IRI in the total and breakdown frequency ranges, in the case of the smartphones that have been fixed; for the smartphones that have been placed at location with realistic settings, a frequency range of 40 to 50 Hz is the most useful range that can be used to estimate IRI.
A selected summary of the multiple regression analysis
Device D (box near gearshift)
Adjusted R square
4.2 Estimation of IRI from the magnitudes calculated from the sum of all axes acceleration vibration
4.3 Estimation of IRI from the magnitudes calculated from each axis of acceleration vibration separately
As Figures 10 and11 indicate, better fit of the model can be achieved when considering each axis of acceleration vibration separately as variables, rather than the sum of all axes. Furthermore, when comparing Figure 11 to Figure 10, by device, respectively, Figure 11 shows better R2 both in the total frequency ranges (0 to 50 Hz) as well as in frequency range of 40 to 50 Hz. These two figures also confirm that, for the smartphones that have been placed at locations under realistic settings, the frequency range of 40 to 50 Hz is more appropriate to be used for the estimation of IRI.
To further explore the use of smartphones to estimate road roughness condition from what we have left in our previous study, in this paper, we use smartphones to collect data for our analysis. The smartphones are placed loosely at locations such as inside the driver's shirt front pocket and the box near the vehicle gearshift. Similar to our previous study, after obtaining data from the experiment, data has been checked, filtered, matched with referenced data, and sectioned. The selected sections are then analyzed in frequency domain to calculate magnitudes of the signal in different frequency ranges. The relationship between the magnitudes and road roughness is investigated. The results of the study confirm that road roughness condition is linked to a linear function of magnitude of acceleration and average speed. It has also been revealed that, for the smartphones that have been placed at locations under realistic setting, in particular, vibration signal of the corresponding road pavement condition (roughness) occurs at the frequency range of 40 to 50 Hz. In other words, data from the smartphone acceleration sensors at the frequency range of 40 to 50 Hz is best in expressing the road roughness condition. In regard to the model to be used for the estimation of IRI, it is more accurate to consider each axis of acceleration vibration as an explanatory variable separately.
In our key ongoing and future work, we are considering using many more different types of smartphones and vehicles as well as different realistic smartphone settings in our experiments. With these ongoing and future experiments, we believe that we will be able to understand more features and aspects on the use of smartphones for the estimation of road roughness condition. Additionally, we are also in the process of formulating a simple model and an Android application to estimate road roughness condition from many anonymous smartphones from real road users. After testing and fine tuning, we are planning to conduct a road condition estimation trial, where we would like to involve the participation from real road users, in Vientiane, Laos, in the very near future.
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