226
Views
2
CrossRef citations to date
0
Altmetric
Review Articles

Simulation-based model comparison methodology with application to road accident models

ORCID Icon, , , &
Pages 5340-5366 | Received 02 Dec 2015, Accepted 04 Feb 2016, Published online: 04 Mar 2017

References

  • Aparicio Izquierdo, F., Arenas Ramírez, B., Bernardos Rodríguez, E. (2013). The interurban DRAG-Spain model: The main factors of influence on road accidents in Spain. Research in Transportation Economics 37(1):57–65.
  • Bierlaire, M. (2015). Simulation and optimization: A short review. Transportation Research Part C: Emerging Technologies 55:4–13.
  • Bijleveld, F., Commandeur, J., Gould, Ph., Koopman, S. J. (2008). Model-based measurement of latent risk in time series with applications. Journal of the Royal Statistical Society: Series A 171(1):265– 277.
  • Bijleveld, F., Commandeur, J., Koopman, S. J., Van Montfort, K. (2010). Multivariate non-linear time series modelling of exposure and risk in road safety research. Journal of the Royal Statistical Society 59(1):145–161.
  • Blum, U., Gaudry, M. (2000). The SNUS 2.5-Model Germany. In: Gaudry, M., Lassarre, S., eds. Structural Road Accident Models: The International DRAG Family. Oxford: Elsevier Science, pp. 67–96.
  • Box, G. E., Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B 26(2):211–252.
  • Box, G. E., Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. San Francisco, NC: Holden-D. iv.
  • Chi, G., Quddus, M. A., Huang, A., Levinson, D. (2013). Gasoline price effects on traffic safety in urban and rural areas: Evidence from Minnesota, 1998–2007. Safety Science 59:154–162.
  • Commandeur, J. F., Koopman, S. J. (2007). An Introduction to State Space Time Series Analysis. Oxford: Oxford University Press.
  • Commandeur, J. J., Bijleveld, F. D., Bergel-Hayat, R., Antoniou, C., Yannis, G., Papadimitriou, E. (2013). On statistical inference in time series analysis of the evolution of road safety. Accident Analysis & Prevention 60:424–434.
  • Dadashova, B., Ramírez Arenas, B., McWilliams Mira, J., Izquierdo Aparicio, F. (2014). Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain. Transport Policy 32:203–217.
  • Dupont, E., Commandeur, J. J., Lassarre, S., Bijleveld, F., Martensen, H., Antoniou, C., Giustiniani, G. (2014). Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries. Accident Analysis & Prevention 71:327–336.
  • Durbin, J., Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.
  • Fridstrøm, L. (2000). The TRULS-1 model for Norway. In: Gaudry, M., Lassarre, S., eds. Structural Road Accident Models: The International DRAG Family. Oxford: Elsevier Science, pp. 97–126.
  • Gaudry, M. (1984). DRAG, un modèle de la Demande Routière, des Accidents et de leur Gravitè, appliquè au Quèbec de 1956–1982. Publication 359, Centre de Recherche sur les Transports (CRT), Universitè de Montrèal.
  • Gaudry, M., Himouri, S. (2013). DRAG-ALZ-1, a first model of monthly total road demand, accident frequency, severity and victims, by category and of mean speed on highways, Algeria, 1970-2007. Research in Transportation Economics 37(1):66–78.
  • Gaudry, M., Lassarre, S. (2000). Structural Road Accident Models. The International DRAG Family. Oxford: Elsevier Science.
  • Gaudry, M., Lestage, P., Guèlat, J., Pierre Galvan, P. (2005). TRIO Tutorial, Version 2. Publication CRT-902, Centre de recherche sur les transports, Universitè de Montrèal, 1993, 1994, 2005 et Publication AJD-102, Agora Jules Dupuit, Universitè de Montréal, 20 p. Available at: http://www.e-ajd.net.
  • Hakim, S., Shefer, D., Hakkert, A.S., Hocherman, I. (1991). A critical review of macro models for road accidents. Accident Analysis & Prevention 23(5):379–400.
  • Harvey, A. C. (1981). Time Series Models. Oxford: Philip Allen.
  • Harvey, A. C., Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society: Series A 149:187–227.
  • Harvey, A. C., Scott, A. (1994). Seasonality in dynamic regression models. The Economic Journal 104(427):1324–1345.
  • Hermans, E., Wets, G., Van den Bossche, F. (2006). Frequency and severity of Belgian road traffic accidents studied by state-space methods. Journal of Transportation and Statistics 9(1):63–76.
  • Hillmer, S. C., Tiao, G. C. (1982). An ARIMA-model-based approach to seasonal adjustment. Journal of American Statistical Association 77:63–70.
  • Kroese, D. P., Taimre, T., Botev, Z. I. (2011). Handbook of Monte Carlo Methods. New York: John Wiley & Sons, pp. 706.
  • Liem, T. C., Dagenais, M., Gaudry, M. (2008). LEVEL: The L-1.4 program for BC-GAUHESEQ regression Box-Cox Generalized AUtoregressive HEteroskedastic Single EQuation models. Publication CRT-510, Centre de recherche sur les transports, Universitè de Montrèal, pp. 41.
  • Mannering, F. L., Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research 1:1–22.
  • Maravall, A. (1985). On structural time series models and the characterization of components. Journal of Business and Economic Statistics 3(4):350–355.
  • Maravall, A. (2005). The use of ARIMA models in unobserved-components estimation: An application to Spanish monetary control. In: Dynamic Econometric Modeling: Proceedings of the Third International Symposium in Economic Theory and Econometrics. Vol. 3. Cambridge: Cambridge University Press, pp. 171.
  • McCarthy, P. (2000). The TRACS-CA model for California. In: Gaudry, M., Lassarre, S., eds. Structural Road Accident Models: The International DRAG Family. Oxford: Elsevier Science, pp. 185–204.
  • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics 21(6):1087–1092.
  • Montgomery, D.C. (2000). Design and Analysis of Experiments. 4th ed. New York: John Wiley & Sons.
  • Quddus, M. A. (2008). Time series count data models: An empirical application to traffic accidents. Accident Analysis & Prevention 40(5):1732–1741.
  • Robert, C. P., Casella, G. (2004). Monte Carlo Statistical Methods. New York: Springer, pp. 319.
  • Scuffham, P. A., Langley, J. D. (2002). A model of traffic crashes in New Zealand. Accident Analysis and Prevention 34:673–687.
  • Selukar, R. (2009). Structural Analysis of Time Series Using SAS/ETS UCM Procedure. Paper 306–2009. Cary, NC: SAS Institute Inc.
  • Stathopoulos, A., Karlaftis, M. G. (2003). A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies 11(2):121– 135.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.