Characterising pavement roughness at non-uniform speeds using connected vehicles

References

• Agresti, A. and Finlay, B. , 2009. Statistical methods for the social sciences . 4th ed. Upper Saddle River, NJ: Pearson Prentice Hall.
   Google Scholar
• Alessandroni, G. , et al. , 2017. A study on the influence of speed on road roughness sensing: the SmartRoadSense case. Sensors , 17 (2), 305.10.3390/s17020305
   Web of Science ®Google Scholar
• Bilodeau, J.-P. , Gagnon, L. , and Doré, G. , 2015. Assessment of the relationship between the international roughness index and dynamic loading of heavy vehicles. International Journal of Pavement Engineering , 1–9. doi:10.1080/10298436.2015.1121780.
   Google Scholar
• Bridgelall, R. , 2014a. Connected vehicle approach for pavement roughness evaluation. Journal of Infrastructure Systems , 20 (1), 1–6.
   Web of Science ®Google Scholar
• Bridgelall, R. , 2014b. Inertial sensor sample rate selection for ride quality measures. Journal of Infrastructure Systems , 21 (2), 1–5.
   Web of Science ®Google Scholar
• Bridgelall, R. , 2015. Precision bounds of pavement distress localization with connected vehicle sensors. Journal of Infrastructure Systems , 21 (3), 1–7.
   Web of Science ®Google Scholar
• Bridgelall, R. , and Tolliver, D.D. , 2016. Accuracy enhancement of roadway anomaly localization using connected vehicles. International Journal of Pavement Engineering , 1–7. doi:10.1080/10298436.2016.1162306.
   Google Scholar
• Bridgelall, R. , Rahman, M.T. , Daleiden, J.F. , and Tolliver, D.T. , 2016. Error sensitivity of the connected vehicle approach to pavement performance evaluations. International Journal of Pavement Engineering , 1–6. doi:10.1080/10298436.2016.1162307.
   Google Scholar
• Bridgelall, R. , Rahman, M.T. , Tolliver, D.D. , and Daleiden, J.F. , 2017. Wavelength sensitivity of roughness measurements using connected vehicles. International Journal of Pavement Engineering , 1–7. doi:10.1080/10298436.2017.1316645.
   Google Scholar
• Cochran, W. G. , and Cox, G. M. , 1957. Experimental designs . 2nd ed. New York: Wiley.
   Google Scholar
• Cruz, J. F. , and Castro, J. T. , 2015. Estimating road roughness conditions using ubiquitous smartphones and geographic information systems and its application to road network planning in the Philippines. In: Proceedings of the 11th International Conference of the Eastern Asia Society for Transportation Studies (EASTS 2015) , University of the Philippines, Cebu City, Philippines.
   Google Scholar
• Dawkins, J. , Bevly, D. , Powell, B. , and Bishop, R. , 2011. Investigation of Pavement Maintenance Applications of Intellidrive . Pooled Fund Study, Charlottesville: University of Virginia.
   Google Scholar
• Dennis, E.P. , and Spulber, A. , 2016. Performance-based planning and programming for pavement management . Ann Arbor, MI: Michigan Department of Transportation (MDOT) and Center for Automotive Research (CAR), 63.
   Google Scholar
• Du, Y. , Liu, C. , Wu, D. , and Jiang, S. , 2014. Measurement of international roughness index by using Z-axis accelerometers and GPS. Mathematical Problems in Engineering , 2014, 1–10.
   Web of Science ®Google Scholar
• Gillespie, T. D. , Sayers, M. W. , and Queiroz, C. A. V. , 1986. The international road roughness experiment: establishing correlation and calibration standard for measurement . Technical Report No. 45, Washington, DC: The World Bank, 464.
   Google Scholar
• HPMS . 2016. Highway Performance Monitoring System (HPMS) field manual . Office of highway policy information, Office of Management and Budget (OMB), Washington, DC: Federal Highway Administration, 295.
   Google Scholar
• Hughes, W. J. , 2016. Global Positioning System (GPS) Standard Positioning Service (SPS) performance analysis report. Performance analysis report 94 , T and E Team, Federal Aviation Administration (FAA), Wide Area Augmentation System (WAAS), Washington, DC: Technical Center.
   Google Scholar
• Islam, S. , Buttlar, W.G. , Aldunate, R.G. , and Vavrik, W.R. , 2014. Measurement of pavement roughness using android-based smartphone application. Transportation Research Record , 2457 (1), 30–38.10.3141/2457-04
   Web of Science ®Google Scholar
• Karamihas, S. M. , 2017. Measuring, characterizing, and reporting pavement roughness of low-speed and urban roads . Research in Progress, Washington, DC: Transportation Research Board of the National Academies. Available from: https://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=3404 [Accessed 10 June 2017].
   Google Scholar
• Nomura, T. , and Shiraishi, Y. , 2015. A method for estimating road surface conditions with a smartphone. International Journal of Informatics Society , 7 (1), 29–36.
   Google Scholar
• Papagiannakis, A. T. , 1997. The need for a new pavement roughness index; RIDE. In: International truck & bus meeting & exposition . Washington, DC: Society of Automotive Engineers International, 1–10. doi:10.4271/973267.
   Google Scholar
• Papoulis, A. , 1991. Probalility, random variables, and stochastic processes . New York: McGraw-Hill.
   Google Scholar
• Stribling, J. W. , 2016. Use of smartphones to measure pavement roughness across multiple vehicle types at different speeds . Masters Thesis, Civil Engineering, Urbana: University of Illinois at Urbana-Champaign.
   Google Scholar
• Wermers, L. G. , 1962. Analysis of road roughness of flexible pavements using the kentucky accelerometer . FHWA/IN/JHRP-62/16 Project No. C-36-54BB, Lafayette, IN: Purdue University, 20.10.5703/1288284313610
   Google Scholar