Smoothing regression and impact measures for accidents of traffic flows

References

• F. Amato, M. Pandolfi, A. Alastuey, A. Lozano, J. Contreras González, and X. Querol, Impact of traffic intensity and pavement aggregate size on road dust particles loading, Atmos. Environ. 77 (2013), pp. 711–717.
   Web of Science ®Google Scholar
• S. Ardekani, E. Hauer, and B. Jamei, Traffic impact models. Chapter 7 in Traffic Flow Theory, Oak Bridge National Laboratory Report, 1992.
   Google Scholar
• P. Bickel, C. Chen, J. Kwon, J. Rice, P. Varaiya, and E. van Zwet, Traffic flow on a freeway network. In Nonlinear Estimation and Classification, Springer, 2003, pp. 63–81.
   Google Scholar
• P.J. Bickel, C. Chen, J. Kwon, J. Rice, E. Van Zwet, and P. Varaiya, Measuring traffic. Statistical Science, 2007, pp. 581–597
   Google Scholar
• M.W. Browne, Cross-validation methods, J. Math. Psychol. 44 (2000), pp. 108–132.
   PubMed Web of Science ®Google Scholar
• G.C. Cawley and N.L.C. Talbot, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res. 11 (2010), pp. 2079–2107.
   Web of Science ®Google Scholar
• C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, Freeway performance measurement system: mining loop detector data, Transp. Res. Rec. 1748 (2001), pp. 96–102.
   Google Scholar
• S.Y. Cheung, S. Coleri, B. Dundar, S. Ganesh, C.-W. Tan, and P. Varaiya, Traffic measurement and vehicle classification with single magnetic sensor, Transp. Res. Rec. 1917 (2005), pp. 173–181.
   Google Scholar
• C. Daganzo and C.F. Daganzo, Fundamentals of transportation and traffic operations, Vol. 30, Pergamon Oxford, 1997.
   Google Scholar
• Y.-A. Daraghmi, C.-W. Yi, and T.-C. Chiang, Space-time multivariate negative binomial regression for urban short-term traffic volume prediction. In 12th International Conference on ITS Telecommunications, IEEE, 2012, pp. 35–40.
   Google Scholar
• Y.-A. Daraghmi, C.-W. Yi, and T.-C. Chiang, Mining overdispersed and autocorrelated vehicular traffic volume. In 5th International Conference on Computer Science and Information Technology, IEEE, 2013, pp. 194–200.
   Google Scholar
• I. Fried, Discretization and round-off errors in the finite element analysis of elliptic boundary value problems and eigenvalue problems. Ph.D. diss., Massachusetts Institute of Technology, 1971
   Google Scholar
• T. Fushiki, Estimation of prediction error by using k-fold cross-validation, Stat. Comput. 21 (2011), pp. 137–146.
   Web of Science ®Google Scholar
• A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Analytical Methods for Social Research. Cambridge University Press, 2006.
   Google Scholar
• C. Gu, Smoothing Spline ANOVA Models, Springer, 2013.
   Google Scholar
• C. Gu and P. Ma, Generalized nonparametric mixed-effect models: computation and smoothing parameter selection, J. Comput. Graph. Stat. 14 (2005), pp. 485–504.
   Web of Science ®Google Scholar
• T. Hastie, R. Tibshirani, J.H. Friedman, and J.H. Friedman, The elements of statistical learning: data mining, inference, and prediction, Vol. 2, Springer, 2009.
   Google Scholar
• J.Z. Huang, Functional anova models for generalized regression, J. Multivar. Anal. 67 (1998), pp. 49–71.
   Web of Science ®Google Scholar
• T. Joachims, Training linear svms in linear time. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 217–226.
   Google Scholar
• P.A. Morettin, A. Pinheiro, and B. Vidakovic, Functional anova. In Wavelets in Functional Data Analysis, Springer, 2017, pp. 71–88.
   Google Scholar
• H.-G. Müller and U. Stadtmüller, Generalized functional linear models, Ann. Stat. 33 (2005), pp. 774–805.
   Web of Science ®Google Scholar
• R.B. Noland and M.A. Quddus, Congestion and safety: A spatial analysis of London, Trans. Res. Part A Policy Pract. 39 (2005), pp. 737–754.
   Web of Science ®Google Scholar
• A. Pradhan, Support vector machine-a survey, Int. J. Emerg. Technol. Adv. Eng. 2 (2012), pp. 82–85.
   Google Scholar
• A.E. Retallack and B. Ostendorf, Current understanding of the effects of congestion on traffic accidents, Int. J. Environ. Res. Public. Health. 16 (2019), pp. 3400.
   PubMed Web of Science ®Google Scholar
• M.A.P. Taylor, J.E. Woolley, and R. Zito, Integration of the global positioning system and geographical information systems for traffic congestion studies, Trans. Res. Part C Emerg. Technol. 8 (2000), pp. 257–285.
   Web of Science ®Google Scholar
• J. Twisk and W. de Vente, Attrition in longitudinal studies: how to deal with missing data, J. Clin. Epidemiol. 55 (2002), pp. 329–337.
   PubMed Web of Science ®Google Scholar
• G. Wahba, Bayesian “confidence intervals” for the cross-validated smoothing spline, J. R. Stat. Soc. Ser B (Methodological) 45 (1983), pp. 133–150.
   Google Scholar
• C. Wang, M. Quddus, and S. Ison, A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the uk, Transportmetrica A Trans. Sci. 9 (2013), pp. 124–148.
   Web of Science ®Google Scholar
• C. Wang, M.A. Quddus, and S.G. Ison, Impact of traffic congestion on road accidents: A spatial analysis of the m25 motorway in England, Accid. Anal. Prev. 41 (2009), pp. 798–808.
   PubMed Web of Science ®Google Scholar
• A.Z. Zambom, J.A. Collazos, and R. Dias, Functional data clustering via hypothesis testing k-means, Comput. Stat. 34 (2019), pp. 527–549.
   Web of Science ®Google Scholar
• J. Zhang, H. Jin, Y. Wang, X. Sun, P. Ma, and W. Zhong, Smoothing spline Anova models and their applications in complex and massive datasets, in Topics in Splines and Applications, Y.K.N. Truong and M. Sarfraz, eds., IntechOpen, London, UK, 2018, pp. 63–82.
   Google Scholar