Validation of Smartphone-Based Pavement Roughness Measures

Document Type : Research Article

Authors

1 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Zachry Department of Civil Engineering, Texas A&M University, Texas, USA

3 Manchester Business School, Manchester, United Kingdom

4 Department of Civil and Environment Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Smartphones are equipped with sensors such as accelerometers, gyroscope and GPS in
one cost-effective device with an acceptable level of accuracy. There have been some research studies
carried out in terms of using smartphones to measure the pavement roughness. However, a little attention
has been paid to investigate the validity of the measured pavement roughness by smartphones via other
subjective methods such as the user opinion. This paper aims at calculating the pavement roughness
data with a smartphone using its embedded sensors and investigating its correlation with a user opinion
about the ride quality. In addition, the applicability of using smartphones to assess the pavement surface
distresses is examined. Furthermore, to validate the smartphone sensor outputs objectively, the Road
Surface Profiler is applied. Finally, a good roughness model is developed which demonstrates an
acceptable level of correlation between the pavement roughness measured by smartphones and the ride
quality rated by users.

Highlights

[1] ASTM Standard E867, Standard Terminology Relating to Vehicle-Pavement Systems, 2012.

[2] P. Mohan, V.N. Padmanabhan, R. Ramjee, Nericell: rich monitoring of road and traffic conditions using mobile smartphones, in: Proceedings of the 6th ACM conference on Embedded network sensor systems, ACM, 2008, pp. 323-336.

[3] R. Bhoraskar, N. Vankadhara, B. Raman, P. Kulkarni, Wolverine: Traffic and road condition estimation using smartphone sensors, in: Communication Systems and Networks (COMSNETS), 2012 Fourth International Conference on, IEEE, 2012, pp. 1-6.

[4] N. Abulizi, A. Kawamura, K. Tomiyama, S. Fujita, Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS, Journal of Traffic and Transportation Engineering (English Edition), 3(5) (2016) 398-411.

[5] W. Chen, J. Yuan, M. Li, Application of GIS/GPS in Shanghai Airport pavement management system, Procedia Engineering, 29 (2012) 2322-2326.

[6] S.M. Bazlamit, H.S. Ahmad, T.I. Al-Suleiman, Pavement Maintenance Applications using Geographic Information Systems, Procedia Engineering, 182 (2017) 83-90.

[7] A. Ozden, A. Faghri, M. Li, K. Tabrizi, Evaluation of Synthetic Aperture Radar satellite remote sensing for pavement and infrastructure monitoring, Procedia Engineering, 145 (2016) 752-759.

[8] J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, H. Balakrishnan, The pothole patrol: using a mobile sensor network for road surface monitoring, in: Proceedings of the 6th international conference on Mobile systems, applications, and services, ACM, 2008, pp. 29-39.

[9] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs, L. Selavo, Real time pothole detection using android smartphones with accelerometers, in: Distributed Computing in Sensor Systems and Workshops (DCOSS), 2011 International Conference on, IEEE, 2011, pp. 1-6.

[10] A. Mahmoudzadeh, S.F. Yeganeh, A. Golroo, Kinect, a novel cutting edge tool in pavement data collection, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1) (2015) 425.

[11] L. González, F. Martínez, M.R. Carlos, Identifying roadway surface disruptions based on accelerometer patterns, IEEE Latin America Transactions, 12(3) (2014) 455-461.

[12] P. Aksamit, M. Szmechta, Distributed, mobile, social system for road surface defects detection, in: Computational Intelligence and Intelligent Informatics (ISCIII), 2011 5th International Symposium on, IEEE, 2011, pp. 37-40.

[13] F. Seraj, B.J. van der Zwaag, A. Dilo, T. Luarasi, P. Havinga, RoADS: A road pavement monitoring system for anomaly detection using smart phones, in: Big data analytics in the social and ubiquitous context, Springer, 2014, pp. 128-146.

[14] Y.-c. Tai, C.-w. Chan, J.Y.-j. Hsu, Automatic road anomaly detection using smart mobile device, in: conference on technologies and applications of artificial intelligence, Hsinchu, Taiwan, 2010.

[15] G. Alessandroni, L. Klopfenstein, S. Delpriori, M. Dromedari, G. Luchetti, B. Paolini, A. Seraghiti, E. Lattanzi, V. Freschi, A. Carini, Smartroadsense: Collaborative road surface condition monitoring, Proceedings of the UBICOMM, (2014) 210-215.

[16] V. Douangphachanh, H. Oneyama, A study on the use of smartphones for road roughness condition estimation, Journal of the Eastern Asia Society for Transportation Studies, 10 (2013) 1551-1564.

[17] V. Douangphachanh, H. Oneyama, A study on the use of smartphones under realistic settings to estimate road roughness condition, EURASIP Journal on Wireless Communications and Networking, 2014(1) (2014) 114.

[18] ProVal version 3.5 [Computer software]. Transtec Group, Austin, TX.

[19] S. Islam, W. Buttlar, R. Aldunate, W. Vavrik, Measurement of pavement roughness using android-based smartphone application, Transportation Research Record: Journal of the Transportation Research Board, (2457) (2014) 30-38.

[20] H. Zeng, Identifying Deficient Pavement Sections using an Improved Acceleration-based Metric, in: Pavement Evaluation Conference 2014, 2014.

[21] T. Hanson, C. Cameron, E. Hildebrand, Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values, Canadian Journal of Civil Engineering, 41(9) (2014) 819-827.

[22] T. Fwa, K. Gan, Bus-ride panel rating of pavement serviceability, Journal of transportation engineering, 115(2) (1989) 176-191.

[23] MnDOT (2009), Minnesota Department of Transportation, Pavement Condition Executive Summary, Report No. MnDOT/OMRR-PM--2009-01.

[24] A. Golroo, S. Tighe, Developing an overall combined condition index for pervious concrete pavements using a specific panel rating method, Transportation Research Record: Journal of the Transportation Research Board, (2153) (2010) 40-48.

[25] A. Golroo, S.L. Tighe, Development of Panel Rating Protocol and Condition Evaluation Model for Pervious Concrete Pavement, Journal of Transportation Engineering, 138(3) (2011) 315-323.

Keywords

References

[1] ASTM Standard E867, Standard Terminology Relating to Vehicle-Pavement Systems, 2012.
[2] P. Mohan, V.N. Padmanabhan, R. Ramjee, Nericell: rich monitoring of road and traffic conditions using mobile smartphones, in: Proceedings of the 6th ACM conference on Embedded network sensor systems, ACM, 2008, pp. 323-336.
[3] R. Bhoraskar, N. Vankadhara, B. Raman, P. Kulkarni, Wolverine: Traffic and road condition estimation using smartphone sensors, in: Communication Systems and Networks (COMSNETS), 2012 Fourth International Conference on, IEEE, 2012, pp. 1-6.
[4] N. Abulizi, A. Kawamura, K. Tomiyama, S. Fujita, Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS, Journal of Traffic and Transportation Engineering (English Edition), 3(5) (2016) 398-411.
[5] W. Chen, J. Yuan, M. Li, Application of GIS/GPS in Shanghai Airport pavement management system, Procedia Engineering, 29 (2012) 2322-2326.
[6] S.M. Bazlamit, H.S. Ahmad, T.I. Al-Suleiman, Pavement Maintenance Applications using Geographic Information Systems, Procedia Engineering, 182 (2017) 83-90.
[7] A. Ozden, A. Faghri, M. Li, K. Tabrizi, Evaluation of Synthetic Aperture Radar satellite remote sensing for pavement and infrastructure monitoring, Procedia Engineering, 145 (2016) 752-759.
[8] J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, H. Balakrishnan, The pothole patrol: using a mobile sensor network for road surface monitoring, in: Proceedings of the 6th international conference on Mobile systems, applications, and services, ACM, 2008, pp. 29-39.
[9] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs, L. Selavo, Real time pothole detection using android smartphones with accelerometers, in: Distributed Computing in Sensor Systems and Workshops (DCOSS), 2011 International Conference on, IEEE, 2011, pp. 1-6.
[10] A. Mahmoudzadeh, S.F. Yeganeh, A. Golroo, Kinect, a novel cutting edge tool in pavement data collection, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1) (2015) 425.
[11] L. González, F. Martínez, M.R. Carlos, Identifying roadway surface disruptions based on accelerometer patterns, IEEE Latin America Transactions, 12(3) (2014) 455-461.
[12] P. Aksamit, M. Szmechta, Distributed, mobile, social system for road surface defects detection, in: Computational Intelligence and Intelligent Informatics (ISCIII), 2011 5th International Symposium on, IEEE, 2011, pp. 37-40.
[13] F. Seraj, B.J. van der Zwaag, A. Dilo, T. Luarasi, P. Havinga, RoADS: A road pavement monitoring system for anomaly detection using smart phones, in: Big data analytics in the social and ubiquitous context, Springer, 2014, pp. 128-146.
[14] Y.-c. Tai, C.-w. Chan, J.Y.-j. Hsu, Automatic road anomaly detection using smart mobile device, in: conference on technologies and applications of artificial intelligence, Hsinchu, Taiwan, 2010.
[15] G. Alessandroni, L. Klopfenstein, S. Delpriori, M. Dromedari, G. Luchetti, B. Paolini, A. Seraghiti, E. Lattanzi, V. Freschi, A. Carini, Smartroadsense: Collaborative road surface condition monitoring, Proceedings of the UBICOMM, (2014) 210-215.
[16] V. Douangphachanh, H. Oneyama, A study on the use of smartphones for road roughness condition estimation, Journal of the Eastern Asia Society for Transportation Studies, 10 (2013) 1551-1564.
[17] V. Douangphachanh, H. Oneyama, A study on the use of smartphones under realistic settings to estimate road roughness condition, EURASIP Journal on Wireless Communications and Networking, 2014(1) (2014) 114.
[18] ProVal version 3.5 [Computer software]. Transtec Group, Austin, TX.
[19] S. Islam, W. Buttlar, R. Aldunate, W. Vavrik, Measurement of pavement roughness using android-based smartphone application, Transportation Research Record: Journal of the Transportation Research Board, (2457) (2014) 30-38.
[20] H. Zeng, Identifying Deficient Pavement Sections using an Improved Acceleration-based Metric, in: Pavement Evaluation Conference 2014, 2014.
[21] T. Hanson, C. Cameron, E. Hildebrand, Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values, Canadian Journal of Civil Engineering, 41(9) (2014) 819-827.
[22] T. Fwa, K. Gan, Bus-ride panel rating of pavement serviceability, Journal of transportation engineering, 115(2) (1989) 176-191.
[23] MnDOT (2009), Minnesota Department of Transportation, Pavement Condition Executive Summary, Report No. MnDOT/OMRR-PM--2009-01.
[24] A. Golroo, S. Tighe, Developing an overall combined condition index for pervious concrete pavements using a specific panel rating method, Transportation Research Record: Journal of the Transportation Research Board, (2153) (2010) 40-48.
[25] A. Golroo, S.L. Tighe, Development of Panel Rating Protocol and Condition Evaluation Model for Pervious Concrete Pavement, Journal of Transportation Engineering, 138(3) (2011) 315-323.
AUT Journal of Civil Engineering
Volume 1, Issue 2
December 2017
Pages 135-144

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