Pavement friction management – artificial neural network approach

References

• Duddu, V. and Pulugurtha, S. , 2012. Neural networks to estimate crashes at zonal level for transportation planning. In: European Conference 2012 , 8–10 October, Glasgow, Scotland. London: Association for European Transport [online]. Available from: http://abstracts.aetransport.org/paper/index/id/3919/confid/18 [Accessed 5 December 2016].
   Google Scholar
• Federal Highway Administration , 2010. Technical Advisory 5040.38 [online]. Available from: https://www.fhwa.dot.gov/pavement/t504038.cfm [Accessed 28 December 2014].
   Google Scholar
• Federal Highway Administration , 2014. Highway Safety Improvement Program (HSIP) [online]. Available from: http://safety.fhwa.dot.gov/hsip/ [Accessed 3 January 2015].
   Google Scholar
• Flintsch, G.W. and Chen, C. , 2004. Soft computing application in infrastructure management. Journal of Infrastructure Systems , 10, 157–166.10.1061/(ASCE)1076-0342(2004)10:4(157)
   Google Scholar
• Flintsch, G.W. , et al. , 1998. Artificial neural network based project selection level pavement management system. 4th International Conference on Managing Pavements , 17–21 May, Durbin, South Africa. Pretoria: University of South Africa, 451–464.
   Google Scholar
• Flintsch, G.W. , et al. , 2010. Speed adjustment factors for locked-wheel skid trailer measurements. Transportation Research Record: Journal of the Transportation Research Board , 2155, 117–123.10.3141/2155-13
   Web of Science ®Google Scholar
• Flintsch, G.W. , et al. , 2012. The little book of tire pavement friction [online]. Available from: https://secure.hosting.vt.edu/www.apps.vtti.vt.edu/1-pagers/CSTI_Flintsch/The%20Little%20Book%20of%20Tire%20Pavement%20Friction.pdf [Accessed 30 July 2013].
   Google Scholar
• Gan, A. , et al. , 2012. Standardization of crash analysis in Florida . Tallahassee, FL: Florida Department of Transportation, Contract No. BDK80 977-10.
   Google Scholar
• Hall, J.W. , et al. , 2009. Guide for pavement friction . Washington, DC: National Cooperative Highway Research Program, Transportation Research Board of the National Academies.
   Google Scholar
• Jafari, S.A. , et al. , 2013. Prediction of road traffic death rate using neural networks optimised by genetic algorithm . International Journal of Injury Control and Safety Promotion, 22 (2) [online]. 10.1080/17457300.2013.857695.
   PubMed Web of Science ®Google Scholar
• Khdair, R.M. , 2006. Signature recognition using neural network . Master’s Thesis. Near East University.
   Google Scholar
• Kunt, M.M. , Aghayana, I. , and Noiib, N. , 2012. Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport , 26 (4), 353–366. 10.3846/16484142.2011.635465.
   Web of Science ®Google Scholar
• McCullough, B.V. and Hankins, K.D. , 1966. Skid resistance guidelines for surface improvements on Texas highways. Highway Research Record , 131, 204–217.
   Google Scholar
• Moghaddam, F.R. , Afandizadeh, Sh. and Ziyadi, M. , 2011. Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering , 9 (1), 41–48.
   Web of Science ®Google Scholar
• Najafi, S. , 2010. Evaluation of continuous friction measuring equipment (CFME) for supporting pavement friction management programs [online]. Thesis. Virginia Polytechnic Institute and State University. Available from: http://scholar.lib.vt.edu/theses/available/etd-12132010-111100/unrestricted/Najafi_Shahriar_2010.pdf [Accessed 15 February 2015].
   Google Scholar
• Najafi, S. , et al. , 2011. Implementation of cross-correlation to compare continuous friction measuring equipment (CFME). In: Transportation Research Board 90th Annual Meeting Compendium of Papers DVD , 23–27 January. Washington, DC: Transportation Research Board, Paper No. 11-2083.
   Google Scholar
• Najafi, S. , Flintsch, G.W. , and McGhee, K.K. , 2013. Assessment of operational characteristics of continuous friction measuring equipment (CFME). International Journal of Pavement Engineering , 14 (8), 706–714.10.1080/10298436.2012.667097
   Web of Science ®Google Scholar
• Najafi, S. , Flintsch, G.W. , and Medina, A. , 2014. Case study on the evaluation of the effect of tire-pavement friction on the rate of roadway crashes. In: Transportation Research Board 93rd Annual Meeting Compendium of Papers [DVD] , 12–16 January. Washington, DC: Transportation Research Board, Paper No. 14-5617.
   Google Scholar
• Najafi, S. , Flintsch, G.W. , and Medina, A. , 2015. Linking roadway crashes and tire–pavement friction: A case study. International Journal of Pavement Engineering [online]. 10.1080/10298436.2015.1039005.
   Web of Science ®Google Scholar
• National Oceanic and Atmospheric Administration , 2015. Climate data online: Dataset discovery [online]. Available from: www.ncdc.noaa.gov/cdo-web/datasets [Accessed 30 August 2015].
   Google Scholar
• Riedmiller, M. and Braun, H. , 1993. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: IEEE International Conference on Neural Networks , 28 March–1 April. San Francisco, CA: IEEE, 586–591. 10.1109/ICNN.1993.298623.
   Google Scholar
• Rizenbergs, R.L. , Burchett, J.L. , and Napier, C.T. , 1972. Skid resistance of pavements, Part II . Lexington: Kentucky Department of Highways, Report No. KYHPR-64-24.
   Google Scholar
• Shekharan, A.R. , 1998. Effect of Noisy data on pavement performance prediction by artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board , 1643, 7–13.10.3141/1643-02
   Google Scholar
• Somers, M. and Casal, J. , 2009. Using artificial neural networks to model nonlinearity: The case of the job satisfaction–job performance relationship. Organizational Research Methods , 12 (3), 403–417. 10.1177/1094428107309326.
   Web of Science ®Google Scholar
• Suman, S.K. and Sinha, S. , 2012. Pavement condition forecasting through artificial neural network modelling. International Journal of Emerging Technology and Advanced Engineering , 2 (11), 474–478.
   Google Scholar
• Thube, D.T. , 1997. Artificial neural network (ANN) based pavement deterioration models for low volume roads in India. International Journal of Pavement Research and Technology , 5 (2), 115–120.
   Google Scholar
• United States Department of Transportation , 2013. Moving ahead for progress in the 21st century act (MAP-21) [online]. Available from: http://www.dot.gov/map21 [Accessed 3 January 2015].
   Google Scholar