Continuous, response-based road roughness measurements utilising data harvested from telematics device sensors

References

• Alessandroni, G., et al., 2014. SmartRoadSense: collaborative road surface condition monitoring. In: the eighth international conference on mobile ubiquitous computing, systems, services and technologies (UBICOMM). Rome: IARIA, 210–215.
   Google Scholar
• ArcGIS Pro, 2017. Environmental Systems Research Institute Inc.
   Google Scholar
• ASTM International, 2012. Standard terminology relating to vehicle-pavement systems. West Conshohocken, PA: ASTM International. (ASTM E867-06).
   Google Scholar
• ASTM Standard E950-98. Re-approved 2004. Standard test method for measuring the longitudinal profile of travelled surfaces with an accelerometer established inertial profiling reference. ASTM International, 100 Bar Harbor Drive, West Conshohocken, PA, United States of America.
   Google Scholar
• Belzowski, B.M., and Cook, S.J., 2014. Integrated mobile observations 2.0. project final report. Lansing, MI: The Regents of the University of Michigan. 1–39. (UMTRI-2014-10).
   Google Scholar
• Blais, J.A.R., 2010. Least squares for practitioners [online]. Mathematical Problems in Engineering. Available from: https://www.hindawi.com/journals/mpe/2010/508092/ [Accessed 16 July 2017].
   Google Scholar
• Bridgelall, R., and Daleiden, J, 2015. Assessing pavement roughness in urban environments. In: Presentation at the 27th Annual Road Profile Users’ Group (RPUG) Meeting, Raleigh. North Carolina.
   Google Scholar
• Chen, K., et al., 2011. Road condition monitoring using on-board three-axis accelerometer and GPS sensor. In: 6th International ICST Conference on Communications and Networking in China. Harbin: IEEE, 1032–1037.
   Google Scholar
• COTO, F, 2007. Guidelines for network level measurement of road roughness version 1.1. Pretoria: Committee of Transport Officials (COTO).
   Google Scholar
• Douangphachanh, V., and Oneyama, H, 2013. A study on the use of smartphones for road roughness condition estimation. Journal of the Eastern Asia Society for Transportation Studies, 10, 1551–1564.
   Google Scholar
• González, A., et al., 2008. The use of vehicle acceleration measurements to estimate road roughness. Vehicle System Dynamics, 46 (6), 483–499. doi: 10.1080/00423110701485050
   Web of Science ®Google Scholar
• Gregg, L.E., and Foy, W.S., 1955. Triaxial acceleration analysis applied to the evaluation of pavement riding qualities. In: Annual Meeting of the Highway Research Board. Washington, DC: Highway Research Board.
   Google Scholar
• Higgins, J, 2010. The radical statistician: unleashing the power of applied statistics in the real world. Sacramento: Jim Higgens.
   Google Scholar
• Hoffmann, M., Mock, M., and May, M, 2013. Road-quality classification and bump detection with bicycle-mounted smartphones. In: 3rd International Conference on Ubiquitous Data Mining (UDM). Beijing: CEUR-WS, 39–43.
   Google Scholar
• Hudson, W.R., 1981. Road roughness: its elements and measurement. transportation research record 836. Washington, DC: Transportation Research Board, National Research Council.
   Google Scholar
• Jones, H., and Forslof, L., 2014. Roadroid continuous road condition monitoring with smart phones. In: 5th SARF/IRF Regional Conference. Pretoria. (Vol 24).
   Google Scholar
• Lakušić, S., Brčić, D., and Tkalčević Lakušić, V.T., 2011. Analysis of vehicle vibrations – new approach to rating pavement condition of Urban roads. PROMET-Traffic&Transportation, 23 (6), 485–494. doi: 10.7307/ptt.v23i6.183
   Web of Science ®Google Scholar
• Montgomery, D.C., and Runger, G.C., 2006. Applied statistics and probability for engineers. 4th ed. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Perttunen, M., et al., 2011. Distributed road surface condition monitoring using mobile phones. In: 8th International Conference on Ubiquitous Intelligence and Computing (UIC). Berlin: Springer, 64–78.
   Google Scholar
• Rizenbergs, R.L., 1965. Accelerometer method of riding-quality testing. Lexington, KY: Department of Highways.
   Google Scholar
• Ross, S.M, 2009. Introduction to probability and statistics for engineers and scientists. 4th ed. San Diego: Elsevier.
   Google Scholar
• Ryan, T.P, 2007. Modern engineering statistics. 1st ed. Hoboken: John Wiley & Sons.
   Google Scholar
• Sayers, M.W., 1989. Two quarter-car models for defining road roughness: IRI and HRI. Washington, DC, United States of America: Transportation Research Record 1215, 165–172.
   Google Scholar
• Sayers, M.W., Gillespie, T.D., and Paterson, W.D.O., 1986b. Guidelines for conducting and calibrating road roughness measurements. Washington, DC: The World Bank. (World Bank Technical Paper Number 46).
   Google Scholar
• Sayers, M.W., Gillespie, T.D., and Queiroz, C.A.V., 1986a. The international road roughness experiment: establishing correlation and a calibration standard for measurements. Washington, DC: The World Bank. (World Bank Technical Paper Number 45).
   Google Scholar
• Sayers, M.W., and Karamihas, S.M., 1998. The little book of profiling: basic information about measuring and interpreting road profiles. Ann Harbor, MI: University of Michigan.
   Google Scholar
• Schlotjes, M.R., Visser, A., and Bennett, C, 2014. Evaluation of a smartphone roughness meter. In: 33rd southern african transport conference (SATC), Pretoria: SATC.
   Google Scholar
• SRT, 2017. Road surface profiler (RSP) [online]. Available from: http://www.srt.co.za/index.php?option=com_content&view=article&id=5:rsp&catid=1:on-site&Itemid=5 [Accessed 20 November 2017].
   Google Scholar
• Tomiyama, K., et al., 2012. A mobile profilometer for road surface monitoring by use of accelerometers. In: 7th symposium on pavement surface characteristics (SURF). Norfolk, Virginia, 1–10.
   Google Scholar
• Tracker Connect Pty Ltd, 2017a. SkyTrax Accelerometer Sign Convention. [Image].
   Google Scholar
• Tracker Connect Pty Ltd, 2017b. SkyTrax Telematics Device. [Image].
   Google Scholar
• Van As, S.C, 2008. Applied statistics for civil engineers. Pretoria: University of Pretoria.
   Google Scholar
• Wolfram MathWorld, 2017. Least Squares Fitting—Polynomial [online]. Mathworld.wolfram.com. Available from: http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html [Accessed 16 July 2017].
   Google Scholar