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The Scientific World Journal
Volume 2014, Article ID 152375, 12 pages
http://dx.doi.org/10.1155/2014/152375
Research Article

Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

1Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
2Universidad Nacional de Chimborazo, 33730880 Riobamba, Ecuador

Received 26 April 2014; Revised 29 July 2014; Accepted 14 August 2014; Published 28 August 2014

Academic Editor: Cagdas Hakan Aladag

Copyright © 2014 Lida Barba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Abellán, G. López, and J. de Oña, “Analysis of traffic accident severity using decision rules via decision trees,” Expert Systems with Applications, vol. 40, no. 15, pp. 6047–6054, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Chang and J. Chien, “Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model,” Safety Science, vol. 51, no. 1, pp. 17–22, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. J. de Oña, G. López, R. Mujalli, and F. J. Calvo, “Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks,” Accident Analysis and Prevention, vol. 51, pp. 1–10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Fogue, P. Garrido, F. J. Martinez, J. Cano, C. T. Calafate, and P. Manzoni, “A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms,” Expert Systems with Applications, vol. 40, no. 1, pp. 323–336, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. M. A. Quddus, “Time series count data models: an empirical application to traffic accidents,” Accident Analysis and Prevention, vol. 40, no. 5, pp. 1732–1741, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. J. F. Commandeur, F. D. Bijleveld, R. Bergel-Hayat, C. Antoniou, G. Yannis, and E. Papadimitriou, “On statistical inference in time series analysis of the evolution of road safety,” Accident Analysis and Prevention, vol. 60, pp. 424–434, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Antoniou and G. Yannis, “State-space based analysis and forecasting of macroscopic road safety trends in Greece,” Accident Analysis and Prevention, vol. 60, pp. 268–276, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Weijermars and P. Wesemann, “Road safety forecasting and ex-ante evaluation of policy in the Netherlands,” Transportation Research A: Policy and Practice, vol. 52, pp. 64–72, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. A. García-Ferrer, A. de Juan, and P. Poncela, “Forecasting traffic accidents using disaggregated data,” International Journal of Forecasting, vol. 22, no. 2, pp. 203–222, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Gençay, F. Selçuk, and B. Whitcher, An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press, 2002. View at MathSciNet
  11. N. Abu-Shikhah and F. Elkarmi, “Medium-term electric load forecasting using singular value decomposition,” Energy, vol. 36, no. 7, pp. 4259–4271, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Sun and J. Hahn, “Parameter reduction for stable dynamical systems based on Hankel singular values and sensitivity analysis,” Chemical Engineering Science, vol. 61, no. 16, pp. 5393–5403, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Gu and H. Wang, “Fuzzy prediction of chaotic time series based on singular value decomposition,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 1171–1185, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. X. Weng and J. Shen, “Classification of multivariate time series using two-dimensional singular value decomposition,” Knowledge-Based Systems, vol. 21, no. 7, pp. 535–539, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. N. Hara, H. Kokame, and K. Konishi, “Singular value decomposition for a class of linear time-varying systems with application to switched linear systems,” Systems and Control Letters, vol. 59, no. 12, pp. 792–798, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. K. Kavaklioglu, “Robust electricity consumption modeling of Turkey using singular value decomposition,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 268–276, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. W. X. Yang and P. W. Tse, “Medium-term electric load forecasting using singular value decomposition,” NDT & E International, vol. 37, pp. 419–432, 2003. View at Google Scholar
  18. K. Kumar and V. K. Jain, “Autoregressive integrated moving averages (ARIMA) modelling of a traffic noise time series,” Applied Acoustics, vol. 58, no. 3, pp. 283–294, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Hassan, “ARIMA and regression models for prediction of daily and monthly clearness index,” Renewable Energy, vol. 68, pp. 421–427, 2014. View at Google Scholar
  20. P. Narayanan, A. Basistha, S. Sarkar, and S. Kamna, “Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India,” Comptes Rendus Geoscience, vol. 345, no. 1, pp. 22–27, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Soni, S. Kapoor, K. S. Parmar, and D. G. Kaskaoutis, “Statistical analysis of aerosols over the gangetichimalayan region using ARIMA model based on long-term MODIS observations,” Atmospheric Research, vol. 149, pp. 174–192, 2014. View at Google Scholar
  22. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Yang, J. Yuan, J. Yuan, and H. Mao, “A modified particle swarm optimizer with dynamic adaptation,” Applied Mathematics and Computation, vol. 189, no. 2, pp. 1205–1213, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. M. S. Arumugam and M. Rao, “On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 324–336, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. B. K. Panigrahi, V. Ravikumar Pandi, and S. Das, “Adaptive particle swarm optimization approach for static and dynamic economic load dispatch,” Energy Conversion and Management, vol. 49, no. 6, pp. 1407–1415, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, “A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing Journal, vol. 11, no. 4, pp. 3658–3670, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Jiang, H. Ling, J. Yan, B. Li, and Z. Li, “Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 194730, 8 pages, 2013. View at Publisher · View at Google Scholar
  28. J. Chen, Y. Ding, and K. Hao, “The bidirectional optimization of carbon fiber production by neural network with a GA-IPSO hybrid algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 768756, 16 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Zhou, Z. Duan, Y. Li, J. Deng, and D. Yu, “PSO-based neural network optimization and its utilization in a boring machine,” Journal of Materials Processing Technology, vol. 178, no. 1–3, pp. 19–23, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. M. A. Mohandes, “Modeling global solar radiation using Particle Swarm Optimization (PSO),” Solar Energy, vol. 86, no. 11, pp. 3137–3145, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. L. F. De Mingo López, N. Gómez Blas, and A. Arteta, “The optimal combination: grammatical swarm, particle swarm optimization and neural networks,” Journal of Computational Science, vol. 3, no. 1-2, pp. 46–55, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Yazgan and I. H. Cavdar, “A comparative study between LMS and PSO algorithms on the optical channel estimation for radio over fiber systems,” Optik, vol. 125, no. 11, pp. 2582–2586, 2014. View at Google Scholar
  33. M. Riedmiller and H. Braun, “A direct adaptive me thod for faster backpropagation learning: the RPROP algorithm,” in Proceedings of the IEEE International Conference of Neural Networks, E. H. Ruspini, Ed., pp. 586–591, 1993.
  34. C. Igel and M. Hüsken, “Empirical evaluation of the improved Rprop learning algorithms,” Neurocomputing, vol. 50, pp. 105–123, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  36. L. Aburto and R. Weber, “Improved supply chain management based on hybrid demand forecasts,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 136–144, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Khashei and M. Bijari, “A new hybrid methodology for nonlinear time series forecasting,” Modelling and Simulation in Engineering, vol. 2011, Article ID 379121, 5 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. R. A. Yafee and M. McGee, An Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS, Academic Press, New York, NY, USA, 2000.
  39. TS. Shores, Applied Linear Algebra and Matrix Analysis, Springer, 2007.
  40. P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer, Berlin, Germany, 2nd edition, 2002.
  41. J. A. Freeman and D. M. Skapura, Neural Networks, Algorithms, Applications, and Programming Techniques, Addison-Wesley, 1991.
  42. R. C. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001.
  43. Conaset, 2014, http://www.conaset.cl.
  44. K. Hipel and A. McLeod, Time Series Modelling of Water Resources and Environmental Systems, Elsevier, 1994.