A Survey on Road Accident Prediction Techniques Based on Various Methodologies

Authors

  • Sonam Singh  Sheat College of Engineering, Varanasi, India
  • Shailesh Singh  Sheat College of Engineering, Varanasi, India

Keywords:

Bartering, SGD, ICON3, Matrix Factorization, The Binary Value Exchange Model, Circular Exchange Of A Single Item

Abstract

This may be understood as a conflict between the self-interest of its members and the greater good of society as a whole in large-scale dispersed ecosystems Mechanisms that give incentives and encourage cooperation are often required to control the participants’ conduct to minimize the possibly unfavorable availability consequences that may follow from individual activities. Economics has a long and varied history of ways to encourage collaboration. Bartering incentive patterns provide an ideal basis for a simple and resilient kind of trade for re-allocating resources in this thesis. Bartering is one of the oldest forms of commerce in the world, yet it still amazes us in many ways. The barter system’s success and long-term viability make it a good model to analyze. When it comes to the Internet, bartering is becoming more commonplace. Making trade recommendations for an “online bartering platform” is a lot like making conventional recommendations, especially when it comes to modeling users’ tastes and the attributes of the goods they consume. Some elements, however, make bartering difficulties intriguing and complex, notably the fact that users are both providers and customers, together with a highly dynamic business setting. “It is important to understand not just the preferences of users but also the social dynamics of who trades with whom, and the time dynamics of transactions occurring. In this paper, we will study the ways of analyzing road accident prediction techniques.

References

  1. F.M.O.I. Forensic Medicine Organization of Iran; Statistical Data, Accidents,     online  available on: http://www.lmo.ir/?siteid=1&pageid=1347
  2. A.T. Kashani et al., “A Data Mining Approach to Identify Key Factors of Traffic Injury Severity”, PROMETTraffic& Transportation, 23(1), pp. 11-17, 2011.
  3. L.Y. Chang, H.W. Wang, “Analysis of traffic injury severity: An application of non-parametric classification tree techniques”, Accident Analysis and Prevention, 38(5), pp. 1019-1027, 2016.
  4. S. Yau-Ren et al. “The Application of Data Mining Technology to Build a Forecasting Model for Classification of Road Traffic Accidents”, Mathematical Problems in Engineering, Volume 2015 (2015), pp. 1-8., 2015. F. Babi and K. Zuskáová • Descriptive and Predictive Mining on Road Accidents Data– 92
  5. R. Nayak et al., “Road Crash Proneness Prediction using Data Mining”. Ailamaki, Anastasia & Amer-Yahia , Sihem (Eds.) Proceedings of the 14th International Conference on Extending Database Technology, Association for Computing Machinery (ACM), Upp-sala, Sweden, pp. 521-526, 2019.
  6. V. Shankar, J. Milton, F. Mannering, “Modeling accident frequencies as zero-altered probability processes: An empirical inquiry”, Accident Analysis & Prevention, 29(6), pp. 829-837, 2000.
  7. A. Araar et al., “Mining road traffic accident data to improve safety in Dubai”, Journal of Theoretical and Applied Information Technology, 47(3), pp. 911-927, 2013.
  8. S. Vigneswaran et al., “Efficient Analysis of Traffic Accident Using Mining Techniques”, International Journal of Software and Hardware Research in Engineering, Vol. 2, No. 3, 2014, pp. 110- 118, 2014.
  9. L. Martin et al. “Using data mining techniques to road safety improvement in Spanish roads”, XI Congreso de Ingeniería del Transporte (CIT 2014), Procedia - Social and Behavioral Sciences 160 (2014), pp. 607–614, 2014.
  10. P. Flach et al., “On the road to knowledge: Mining 21 years of UK traffic accident reports”, Data Mining and Decision Support: Aspects of Integration and Collaboration, Springer, pp. 143-155, 2013.
  11. H. Zhang et al., “In-Memory Big Data Management and Processing: A Survey”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 7, pp. 1920–1948, 2015.
  12. J. Hipp, U. Güntzer, G. Nakhaeizadeh, “Algorithms for Association Rule Mining &Mdash; a General Survey and Comparison”, SIGKDD Explor Newsl 2, pp. 58–64, 2020.
  13. A.T. Kashani et al., “A Data Mining Approach to Identify Key Factors of Traffic Injury Severity”, PROMETTraffic& Transportation, 23(1), pp. 11-17, 2021.
  14. P. J. Ossenbruggen, J. Pendharkar et. al., “Roadway safety in rural and small urbanized areas”, Accidents Analysis & Prevention, 33(4), pp. 485-498, 2021.
  15. R. Agrawal, T. Imieliski, A. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, pp. 207–216, 2018.
  16. R. Agrawal, R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases”, Proceedings of the 20th International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 487-499, 2019.
  17. L. Breiman, “Random Forests”, Machine Learning, Vol. 45, pp. 5 - 32, 2021
  18. Savolainen P, Mannering F, Lord D, Quddus M. The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid Anal Prev. 2021;43:1666–76.
  19. Depaire B, Wets G, and Vanhoof K. Traffic accident segmentation utilizing latent class clustering, accident analysis, and prevention, vol. 40. Elsevier; 2018.
  20. Karlaftis M, Tarko A. Heterogeneity considerations in accident modeling. Accid Anal Prev. 2000;30(4):425–33.
  21. Ma J, Kockelman K. Crash frequency and severity modeling using clustered data from Washington state. In: IEEE Intelligent Transportation Systems Conference. Toronto Canadá; 2016.
  22. Jones B, Janssen L, Mannering F. Analysis of the frequency and duration of freeway accidents in Seattle, accident analysis and prevention, vol. 23. Elsevier; 2021.
  23. Miaou SP, Lum H. Modeling vehicle accidents and highway geometric design relationships, accident analysis and prevention, vol. 25. Elsevier; 2020.
  24. Miaou SP. The relationship between truck accidents and geometric design of road sections–Poisson versus negative binomial regressions, accident analysis, and prevention, vol. 26. Elsevier; 1994.
  25. Poch M, Mannering F. Negative binomial analysis of intersection-accident frequencies. J Transp Eng. 1996;122.
  26. Abdel-Aty MA, Radwan AE. Modeling traffic accident occurrence and involvement. Accid Anal Prev Elsevier. 2021;32.
  27. Joshua SC, Garber NJ. Estimating truck accident rate and involvements using linear and Poisson regression models. Transp Plan Technol. 2000;15.
  28. Maher MJ, Summersgill I. A comprehensive methodology for the fitting of predictive accident models. Accid Anal Prev Elsevier. 1996;28.
  29. Chen W, Jovanis P. Method of identifying factors contributing to driver-injury severity in traffic crashes. Transp Res Rec. 2012:1717.
  30. Chang LY, Chen WC. Data mining of tree-based models to analyze freeway accident frequency. J Saf Res Elsevier. 2015;36.
  31. Tan PN, Steinbach M, Kumar V. Introduction to data mining. Pearson Addison-Wesley; 2006.
  32. Abellan J, Lopez G, Ona J. Analysis of traffic accident severity using decision rules via decision trees, vol. vol. 40. Expert System with Applications: Elsevier; 2013.
  33. Rovsek V, Batista M, Bogunovic B. Identifying the key risk factors of traffic accident injury severity on Slovenian roads using a non-parametric classification tree, transport. UK: Taylor and Francis; 2014.
  34. Kashani T, Mohaymany AS, Rajbari A. A data mining approach to identify key factors of traffic injury severity, prompt traffic & transportation, vol. 23; 2011.
  35. Han J, Kamber M. Data Mining: Concepts and Techniques. USA: Morgan Kaufmann Publishers; 2021.
  36. Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97(458):611–31.
  37. Sohn SY. Quality function deployment applied to local traffic accident reduction. Accid Anal Prev 2011;31:751–61.
  38. Hung WT, Wong WG. An algorithm for assessing the risk of traffic accidents. J Saf Res. 2012;33:387–410.
  39. Pardillo-Mayora JM, Domínguez-Lira CA, Jurado-Pina R. Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads. Accid Anal Prev. 2019;42:2018–23.
  40. Vermunt JK, Magidson J. Latent class cluster analysis. In: Hagenaars JA, McCutcheon AL, editors. Advances in latent class analysis. Cambridge: Cambridge University Press; 2012.
  41. Oña JD, López G, Mujalli R, Calvo FJ. Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks, accident analysis, and prevention, vol. 51; 2013.
  42. Kaplan S, Prato CG. Cyclist-motorist crash patterns in Denmark: a latent class clustering approach. Traffic Inj Prev. 2013;14(7):725–33.
  43. Chaturvedi A, Green P, Carroll J. K-modes clustering. J Classif. 2011;18:35–55.
  44. Goodman LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrica. 2020;62.
  45. Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on very large databases; 1994. pp. 487–99.
  46. Akaike H. Factor analysis and AIC. Psychometry. 1987;52:317–32.
  47. Raftery AE. A note on Bayes factors for log-linear contingency table models with vague prior information. J Roy Stat Soc B. 1986;48:249–50.
  48. Fraley C, Raftery AE. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J. 1998;41:578–88.
  49. Wong SC, Leung BSY, Loo BPY, Hung WT, Lo HK. A qualitative assessment methodology for road safety policy strategies. Accid Anal Prev. 2014;36:281–93.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2022-02-28

Issue

Volume 9, Issue 1, January-February-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sonam Singh, Shailesh Singh "A Survey on Road Accident Prediction Techniques Based on Various Methodologies " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 1, pp.345-356, January-February-2022.