Abstract
A well-maintained road network is a crucial factor for sustainable urban development. Over the past few years, researchers have proposed smartphone-based crowdsourced applications as a low-cost effective solution to acquire frequent road surface quality updates. One of the main limitations faced by these applications is that the collected values exhibit significant variations over the conditions under which the road data was collected. This study is an attempt to develop a road roughness monitoring platform using passenger cars that can produce accurate results while reducing the effect of these conditions such as the car type, smartphone model, or its placement. The developed system consists of several features including automatic journey detection, freedom to use any smartphone in any position with or without an active internet connection when collecting data, converging values collected from different sources, and visualizing them in a virtual map. A set of field tests were carried out to evaluate the proposed system based on the road condition, passenger car type, smartphone model, and smartphone placement inside the vehicle. The results show that the proposed solution is effective in predicting accurate values after reducing the effect of these varying factors.
Similar content being viewed by others
References
Couchbase lite. https://www.couchbase.com/products/lite
Pathsense. https://pathsense.com/
National Road Master Plan 2018-2028. Road development authority sri lanka (2018)
Abeywardana, H., Abeywikrama, U., Amarasinghe, P.T., Kumarasinghe, R.: iroads - smartphone-based road condition monitoring
Ahmed, H.U., Hu, L., Yang, X., Bridgelall, R., Huang, Y.: Effects of smartphone sensor variability in road roughness evaluation. International Journal of Pavement Engineering, pp. 1–6 (2021)
Aleadelat, W., Ksaibati, K.: Estimation of pavement serviceability index through android-based smartphone application for local roads. Transportation Research Record 2639(1), 129–135 (2017)
Allouch, A., Koubaa, A., Abbes, T., Ammar, A.: Roadsense: Smartphone application to estimate road conditions using accelerometer and gyroscope. IEEE Sensors J. 17(13), 4231–4238 (2017). https://doi.org/10.1109/jsen.2017.2702739
Bapari, M., Haque, M., Chowdhury, D., Islam, M.J.: Impacts of unplanned urbanization on the socio–economic conditions and environment of pabna municipality bangladesh (2016)
Bhoraskar, R., Vankadhara, N., Raman, B.: Kulkarni, P.: Wolverine: Traffic and road condition estimation using smartphone sensors. 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012). https://doi.org/10.1109/comsnets.2012.6151382 (2012)
Brdar, S., Gonzalez-Velez, H., Truica̧, C.O., Benkner, S., Bajrovic, E., Papadopoulos, A., Novović, O., Grujić, N.: Big Data Processing, Analysis and Applications in Mobile Cellular Networks, pp. 163–185. https://doi.org/10.1007/978-3-030-16272-6_6 (2019)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. https://doi.org/10.1145/2939672.2939785, pp 785–794. Association for Computing Machinery, New York, NY, USA (2016)
Chugh, G., Bansal, D., Sofat, S.: Road condition detection using smartphone sensors: a survey. International Journal of Electronic and Electrical Engineering 7(6), 595–602 (2014)
Costa, D.I.C., Filho, E.P.e.S., Silva, R.F.d., de C. Quaresma Gama, T.D., Cortés, M.I.: Microservice architecture: A tertiary study. In: Proceedings of the 14th Brazilian Symposium on Software Components, Architectures, and Reuse, SBCARS ’20. https://doi.org/10.1145/3425269.3425277, pp 61–70. Association for Computing Machinery, New York, NY, USA (2020)
Dauni, P., Firdaus, M.D., Asfariani, R., Saputra, M.I.N., Hidayat, A.A., Zulfikar, W.B.: Implementation of haversine formula for school location tracking. Journal of Physics: Conference Series 1402(7), 077028 (2019). https://doi.org/10.1088/1742-6596/1402/7/077028
Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M., Schiavina, M.: Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics 125(103312), 103312 (2021)
Douangphachanh, V., Oneyama, H.: A study on the use of smartphones under realistic settings to estimate road roughness condition EURASIP. Journal on Wireless Communications and Networking 2014(1). https://doi.org/10.1186/1687-1499-2014-114 (2014)
Du, Y., Liu, C., Wu, D., Jiang, S.: Measurement of international roughness index by usingz-axis accelerometers and gps. Math. Probl. Eng. 2014, 1–10 (2014). https://doi.org/10.1155/2014/928980
Fontaras, G., Zacharof, N.G., Ciuffo, B.: Fuel consumption and co 2 emissions from passenger cars in europe – laboratory versus real-world emissions. Prog. Energy Combust. Sci. 60, 97–131 (2017). https://doi.org/10.1016/j.pecs.2016.12.004
Harikrishnan, P.M., Gopi, V.P.: Vehicle vibration signal processing for road surface monitoring. IEEE Sensors J. 17(16), 5192–5197 (2017). https://doi.org/10.1109/JSEN.2017.2719865
Islam, S., Buttlar, W.G., Aldunate, R.G., Vavrik, W.R.: Use of cellphone application to measure pavement roughness. T and DI Congress 2014. https://doi.org/10.1061/9780784413586.053 (2014)
Jones, H.: Roadroid continuous road condition monitoring with smart phones (2014)
Kumar, R., Mukherjee, A., Singh, V.P.: Community sensor network for monitoring road roughness using smartphones. Journal of Computing in Civil Engineering 31(3), 04016059 (2017). https://doi.org/10.1061/(asce)cp.1943-5487.0000624
Li, H., Goldberg, C., Yin, H.: Embracing crowdsensing: An enhanced mobile sensing solution for road anomaly detection. ISPRS International Journal of Geo-information 8(9), 412 (2019). https://doi.org/10.3390/ijgi8090412
Li, X., Goldberg, D.W.: Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urban. Syst. 69, 51–62 (2018). https://doi.org/10.1016/j.compenvurbsys.2017.12.005
Lima, L.C., Amorim, V.J.P., Pereira, I.M., Ribeiro, F.N., Oliveira, R.A.R.: Using crowdsourcing techniques and mobile devices for asphaltic pavement quality recognition. In: 2016 VI Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 144–149. https://doi.org/10.1109/SBESC.2016.029 (2016)
Lum, P.S., Shu, L., Bochniewicz, E.M., Tran, T., Chang, L.C., Barth, J., Dromerick, A.W.: Improving accelerometry-based measurement of functional use of the upper extremity after stroke: Machine learning versus counts threshold method. Neurorehabilitation and Neural Repair 34(12), 1078–1087 (2020)
Luu, H.N., Nguyen, N.M., Ho, H.H., Tien, D.N.: Infrastructure and economic development in developing economies. Int. J. Soc. Econ. 46(4), 581–594 (2019). https://doi.org/10.1108/ijse-05-2018-0252
Álvarez Cid-Fuentes, J., Álvarez, P., Amela, R., Ishii, K., Morizawa, R.K., Badia, R.M.: Efficient development of high performance data analytics in python. Future Generation Computer Systems 111, 570–581 (2020). https://doi.org/10.1016/j.future.2019.09.051, https://www.sciencedirect.com/science/article/pii/S0167739X18321393
Medina, J.R., Salim, R., Underwood, B.S., Kaloush, K.: Experimental study for crowdsourced ride quality index estimation using smartphones. Journal of Transportation Engineering, Part B: Pavements 146(4), 04020070 (2020). https://doi.org/10.1061/jpeodx.0000225
Meijer, A., Bolívar, M.P.R.: Governing the smart city: a review of the literature on smart urban governance. Int. Rev. Adm. Sci. 82(2), 392–408 (2016). https://doi.org/10.1177/0020852314564308
Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: Using mobile smartphones for rich monitoring of road and traffic conditions. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys ’08. https://doi.org/10.1145/1460412.1460450, pp 357–358. Association for Computing Machinery, New York, NY, USA (2008)
Nirmal, P., Disanayaka, I., Haputhanthri, D., Wijayasiri, A.: Transportation mode detection using crowdsourced smartphone data. In: 2021 28Th Conference of Open Innovations Association (FRUCT), pp. 341–349. https://doi.org/10.23919/FRUCT50888.2021.9347625 (2021)
Perttunen, M., Mazhelis, O., Cong, F., Kauppila, M., Leppänen, T., Kantola, J., Collin, J., Pirttikangas, S., Haverinen, J., Ristaniemi, T., et al.: Distributed road surface condition monitoring using mobile phones. Ubiquitous Intelligence and Computing Lecture Notes in Computer Science, pp. 64–78. https://doi.org/10.1007/978-3-642-23641-9_8 (2011)
Sattar, S., Li, S., Chapman, M.A.: Road surface monitoring using smartphone sensors: A review. https://doi.org/10.32920/14638491.v1 (2021)
Singh, G., Bansal, D., Sofat, S., Aggarwal, N.: Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive and Mobile Computing 40, 71–88 (2017). https://doi.org/10.1016/j.pmcj.2017.06.002, https://www.semanticscholar.org/paper/0af1a90cc44b6a24c26e707e756397c5d161e4fa
Souza, V.M., Giusti, R., Batista, A.J.: Asfault: a low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning. Pervasive and Mobile Computing 51, 121–137 (2018). https://doi.org/10.1016/j.pmcj.2018.10.008
Staniek, M.: Repeatability of road pavement condition assessment based on three-dimensional analysis of linear accelerations of vehicles. IOP Conference Series: Materials Science and Engineering 356, 012021 (2018). https://doi.org/10.1088/1757-899x/356/1/012021
Staniek, M.: Road pavement condition diagnostics using smartphone-based data crowdsourcing in smart cities. Journal of Traffic and Transportation Engineering (English Edition) 8(4), 554–567 (2021). https://doi.org/10.1016/j.jtte.2020.09.004
Vittorio, A., Rosolino, V., Teresa, I., Vittoria, C.M., Vincenzo, P.G., Francesco, D.M.: Automated sensing system for monitoring of road surface quality by mobile devices. Procedia, Social and Behavioral Sciences 111, 242–251 (2014). https://doi.org/10.1016/j.sbspro.2014.01.057, https://www.sciencedirect.com/science/article/pii/S1877042814000585
Wang, G., Burrow, M., Ghataora, G.: Study of the factors affecting road roughness measurement using smartphones. Journal of Infrastructure Systems 26(3), 04020020 (2020)
Acknowledgements
This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education, Sri Lanka funded by the World Bank. Also, we acknowledge the support received from the Senate Research Committee Grant, University of Moratuwa, Sri Lanka.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gamage, Y.T., Thotawaththa, T.A.I. & Wijayasiri, A. Measuring Road Roughness through Crowdsourcing while Minimizing the Conditional Effects. Int. J. ITS Res. 20, 581–601 (2022). https://doi.org/10.1007/s13177-022-00312-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13177-022-00312-6