On-the-fly community-driven mobile accelerometer data analysis system for road quality assessment

• Autorzy: Badurowicz M. , Cieplak T. , Montusiewicz J.
• Języki publikacji: EN

Abstrakty

EN

In this paper the authors are discussing a community-driven system for reporting via smartphones road acceleration data, processed on-the-fly in the cloud computing system for finding possible road artefacts as well as assessing overall road quality on the driver-friendly RRUI scale. The proposed system uses smartphones mounted in a car with little to no calibration or initial setup. By performing a fast analysis in the cloud, data are made immediately available for other users. The system continuously sends to end users' devices data about road quality issues "in exchange" for acceleration information.

Słowa kluczowe

EN

potholes; accelerometer; roads quality; cloud computing

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2016
• Tom: Vol. 12, no 4
• Strony: 18--27
• Opis fizyczny: Bibliogr. 13 poz., fig., tab.

Twórcy

autor

Badurowicz M.

• Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka Str. 36D, 20-618, Lublin, Poland

autor

Cieplak T.

• Management Faculty, Lublin University of Technology, Nadbystrzycka Str. 38A, 20-618 Lublin, Poland

autor

Montusiewicz J.

• Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka Str. 36D, 20-618, Lublin, Poland

Bibliografia

• 1. Aksamit, P., Szmechta, M. (2011). Distributed, mobile, social system for road surface defects detection. In: Computational Intelligence and Intelligent Informatics (ISCIII) (pp. 37-40).
• 2. Astarita, V., Vaiana, R., Iueleand, T., Maria, V.C., Giofre, V.P., Masi, F.D. (2014). Automated sensing system for monitoring of road surface quality by mobile devices. Procedia - Social and Behavioral Sciences 111, 242-251.
• 3. Badurowicz, M., Montusiewicz, J. (2015) Identifying road artefacts with mobile devices. Information and Software Technologies, vol. 538, Springer
• 4. Ballard, C., et al. (2012). IBM InfoSphere Streams: Assembling Continuous Insight in the Information Revolution. IBM Redbooks.
• 5. Chen, K., Lu, M., Fan, X., Wei, M., Wu, J. (2011). Road condition monitoring using on-board three-axis accelerometer and gps sensor. In: Communications and Networking in China (CHINACOM) (pp. 1032-1037).
• 6. Du, Y., Liu, C., Wu, D., Jiang, S. (2014). Measurement of international roughness index by using z-axis accelerometers and gps. Mathematical Problems in Engineering 2014
• 7. Duda, J., Adamczyk, M. (1999). Instrukcja oceny stanu nawierzchni dróg publicznych w miastach. Generalna Dyrekcja Dróg Publicznych
• 8. Gajewski, W. (2013). Nowoczesne metody diagnostyki stanu, czyli profilaktyka w drogownictwie. Magazyn Autostrady Nr 10, pp. 46-51.
• 9. Hanson, T., Cameron, C., Hildebrand, E. (2014). Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (iri) values. Canadian Journal of Civil Engineering 41(9) (pp. 819-827)
• 10. Hummer, W., Satzger, B., Dustdar, S. (2013). Elastic stream processing in the cloud. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3(5) (pp. 333-345)
• 11. Lundström, J. (2009). Road roughness etimation using available vehicle sensors
• 12. Sun, D., Zhang, G., Zheng, W., Li, K. (2015). Key technologies for big data stream computing
• 13. Turkay, S., Akcay, H. (2015). Spectral modeling of longitudinal road profiles. In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 477-482)