Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 852495, 7 pages
http://dx.doi.org/10.1155/2014/852495
Research Article

Unrecorded Accidents Detection on Highways Based on Temporal Data Mining

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China

Received 3 April 2014; Revised 26 May 2014; Accepted 26 May 2014; Published 15 June 2014

Academic Editor: Hamid Reza Karimi

Copyright © 2014 Shi An 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. E. Krug, “Decade of action for road safety 2011–2020,” Injury, vol. 43, no. 1, pp. 6–7, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Gregoriades and K. C. Mouskos, “Black spots identification through a Bayesian Networks quantification of accident risk index,” Transportation Research C: Emerging Technologies, vol. 28, pp. 28–43, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. O. H. Kwon, M. J. Park, H. Yeo, and K. Chung, “Evaluating the performance of network screening methods for detecting high collision concentration locations on highways,” Accident Analysis and Prevention, vol. 51, pp. 141–149, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. P. T. Savolainen, F. L. Mannering, D. Lord, and M. A. Quddus, “The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives,” Accident Analysis and Prevention, vol. 43, no. 5, pp. 1666–1676, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Depaire, G. Wets, and K. Vanhoof, “Traffic accident segmentation by means of latent class clustering,” Accident Analysis and Prevention, vol. 40, no. 4, pp. 1257–1266, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. M. Pardillo-Mayora, C. A. Domínguez-Lira, and R. Jurado-Piña, “Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads,” Accident Analysis and Prevention, vol. 42, no. 6, pp. 2018–2023, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. B.-J. Park and D. Lord, “Application of finite mixture models for vehicle crash data analysis,” Accident Analysis and Prevention, vol. 41, no. 4, pp. 683–691, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. B.-J. Park, D. Lord, and J. D. Hart, “Bias properties of Bayesian statistics in finite mixture of negative binomial regression models in crash data analysis,” Accident Analysis and Prevention, vol. 42, no. 2, pp. 741–749, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Simoncic, “A Bayesian network model of two-car accidents,” Journal of Transportation and Statistics, vol. 7, no. 2-3, pp. 13–25, 2005. View at Google Scholar · View at Scopus
  10. J. De Oña, R. O. Mujalli, and F. J. Calvo, “Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks,” Accident Analysis and Prevention, vol. 43, no. 1, pp. 402–411, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. R. O. Mujalli and J. De Oña, “A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks,” Journal of Safety Research, vol. 42, no. 5, pp. 317–326, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Chung, K. Jang, S. Madanat, and S. Washington, “Proactive detection of high collision concentration locations on highways,” Transportation Research A: Policy and Practice, vol. 45, no. 9, pp. 927–934, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Chung, D. R. Ragland, S. Madanat, and S. M. Oh, “The continuous risk profile approach for the identification of high collision concentration locations on congested highways,” in Proceedings of the 19th International Symposium on Transportation & Traffic Theory (ISTTT '09), pp. 463–480, 2009.
  14. G. Vandenbulcke, I. Thomas, and L. Int Panis, “Predicting cycling accident risk in Brussels: a spatial case-control approach,” Accident Analysis and Prevention, vol. 62, pp. 341–357, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Zheng, “Empirical analysis on relationship between traffic conditions and crash occurrences,” Procedia-Social and Behavioral Sciences, vol. 43, pp. 302–312, 2012. View at Publisher · View at Google Scholar
  16. A. Hamzehei, E. Chung, and M. Miska, “Pre-crash and non-crash traffic flow trends analysis on motorways,” in Proceedings of the Australasian Transport Research Forum, 2013.
  17. C. F. Daganzo, “The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory,” Transportation Research B: Methodological, vol. 28, no. 4, pp. 269–287, 1994. View at Google Scholar · View at Scopus
  18. C. F. Daganzo, “The cell transmission model, part II: network traffic,” Transportation Research B: Methodological, vol. 29, no. 2, pp. 79–93, 1995. View at Google Scholar · View at Scopus
  19. M. J. Lighthill and G. B. Whitham, “On kinematic waves I: flow movement in long rivers. II: a theory of traffic flow on long crowded roads,” Proceedings of the Royal Society of London A, vol. 229, no. 1178, pp. 281–316, 1955. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  20. P. I. Richards, “Shock waves on the highway,” Operations Research, vol. 4, pp. 42–51, 1956. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. G. Zi-You and L. Ke-Ping, “Evolution of traffic flow with scale-free topology,” Chinese Physics Letters, vol. 22, no. 10, pp. 2711–2714, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. X.-G. Li, Z.-Y. Gao, K.-P. Li, and X.-M. Zhao, “Relationship between microscopic dynamics in traffic flow and complexity in networks,” Physical Review E, vol. 76, no. 1, Article ID 016110, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Zhao, H. Liu, J. Zhang, and H. Li, “Multiple-mode observer design for a class of switched linear systems,” IEEE Transactions on Automation Science and Engineering, 2013. View at Publisher · View at Google Scholar
  24. J.-C. Weng, L.-L. Liu, and B. Du, “ETC data based traffic information mining techniques,” Journal of Transportation Systems Engineering and Information Technology, vol. 10, no. 2, pp. 57–63, 2010. View at Google Scholar · View at Scopus