Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
More options …

Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Tatiana Tambouratzis
  • Department of Industrial Management & Technology, University of Piraeus, 107 Deligiorgi St, Piraeus 185 34, Greece
/ Dora Souliou
  • School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St, Zografou 15780, Greece
/ Miltiadis Chalikias
  • Department of Business Administration, Technological Educational Institution of Peiraius, 250 Thivon and Petrou Ralli Av., 122 44 Egaleo, Greece
/ Andreas Gregoriades
  • Department of Computer Science & Engineering, European University Cyprus, Cyprus
Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0023

Abstract

The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

References

  • [1] AAAM, 1985. Abbreviated Injury Scale 1985. Des Plaines IL: Association for the Advancement of Automotive Medicine.Google Scholar

  • [2] Abdel-Aty M.A., Abdelwahab H.T., 2004. Predicting injury severity levels in traffic crashes: a modeling comparison, Journal of Transportation Engineering, vol. 130, pp. 204-210.Google Scholar

  • [3] Abdelwahab, H.T., Abdel-Aty, M.A., 2001. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalizes, Intersection Transportation Research Record issue 1746, pp. 6-13.Google Scholar

  • [4] Akaike, H., 1974. A new look at the statistical model identification, IEEE Transactions on Automatic Control 19(6): 716-723CrossrefGoogle Scholar

  • [5] Akaike Hirotugu, 1980. Likelihood and the Bayes procedure, in Bernardo, J. M.; et al., Bayesian Statistics, Valencia: University Press, pp. 143-166.Google Scholar

  • [6] Baker S.P., O’Neill B., Haddon Jr W., Long W.B., 1974. The Injury Severity Score: a method for describing patients with multiple injuries and evaluating emergency care, The Journal of Trauma (LippincottWilliams &Wilkins), vol. 14, pp. 187-196.Google Scholar

  • [7] Beshah T., Ejigu D., Kromer P., Snasel V., Platos J., Abraham A., 2012. Learning the Classification of Traffic Accident Types, Fourth International Conference on Intelligent Networking and Collaborative Systems, Bucharest, Romania, September 19th-21st, 2012, pp. 463-468.Google Scholar

  • [8] Breiman L., Friedman J.H., Olshen R.A., Stone, C.J., 1984. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.Google Scholar

  • [9] Breiman Leo, 1996. Bagging predictors. Machine Learning 24(2): 123-140.CrossrefGoogle Scholar

  • [10] Breiman, Leo., 1998 Arcing classifiers, The Annals of Statistics, vol. 26, pp.801-849.Google Scholar

  • [11] Breiman Leo., 2001 Random Forests. Machine Learning, volume 45, pp.5-32.Google Scholar

  • [12] Catell, R.B., 1966. The scree test for the number of factors. Multivariate Behavioral Research, 1,245-276Google Scholar

  • [13] Chang, L-.Y,Wang H.-W., 2006. Analysis of traffic injury severity: An application of non-parametric classification tree techniques, Accident Analysis and Prevention, vol. 38, pp. 1019-1027.Google Scholar

  • [14] Chang L.Y., Chien J.-T, 2013 Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree-model, Safety Science, vol. 51, pp. 17-22.Google Scholar

  • [15] Chong M.M., Abraham A., Paprzycki M., 2004. Traffic accident analysis using decision trees and neural networks, IADIS International Conference on Applied Computing, Portugal, IADIS Press, Pedro Isaias et al. (Eds.), ISBN: 9729894736, Vol. 2, pp. 39-42.Google Scholar

  • [16] Delen D., Sharda R., Bessonov M., 2006. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks, Accident Analysis and Prevention, vol. 38, pp. 434-444.Google Scholar

  • [17] Devijver P.A., Kittler J. 1982. Pattern Recognition: A Statistical Approach, Prentice-Hall, London, U.K.Google Scholar

  • [18] Fx GarcL, Miguel GarcTorres, BeleliBatista, Jos Moreno-Pz, J. Marcos Moreno-Vega: Solving feature subset selection problem by a Parallel Scatter Search. European Journal of Operational Research 169(2): 477-489 (2006)Google Scholar

  • [19] Fisher, R. A., 1936. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7 (2): 179-188.CrossrefGoogle Scholar

  • [20] Gini C., 1909. Concentration and dependency ratios (in Italian). English translation in Rivista di Politica Economica, 87 (1997), 769-789.Google Scholar

  • [21] Gini C., 1912. ”Italian: VariabilitutabilitVariability and Mutability’, C. Cuppini, Bologna, 156 pages. Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).Google Scholar

  • [22] Glover F., 1977. Heuristics for integer programming using surrogate constraints. Decision Sciences, vol. 8, pp. 156-166.CrossrefGoogle Scholar

  • [23] Goodman, SN 1999. Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of Internal Medicine 130: 995-1004Google Scholar

  • [24] Grossberg S., 1987. Competitive learning: from interactive activation to adaptive resonance, Cognitive Science, vol. 11, pp. 23-63.Google Scholar

  • [25] Hall M. A., 1998. Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New ZealandGoogle Scholar

  • [26] Hall Mark, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten 2009; The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.Google Scholar

  • [27] Hardin J., Hilbe J., 2007. Generalized Linear Models and Extensions (2nd edition). College Station: Stata Press.Google Scholar

  • [28] Haykin S., 1999. Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice-Hall, Upper Saddle River, NJ.Google Scholar

  • [29] Heckerman D. 1997. Bayesian Networks for Data Mining. Data Mining and Knowledge discovery, 1(1) : 79-119, 1997.Google Scholar

  • [30] Kaiser, H. F., 1960 The application of electronic computer to factor analysis. Educational and Psychological Measurement, 20, 141-151.Google Scholar

  • [31] Khattak A., Rocha M., 2003. Are SUVs “supremely unsafe vehicles”? Analysis of rollovers and injuries with sport utility vehicles, Transportation Research Record 1840, pp. 167-177.Google Scholar

  • [32] Kohavi R., John G. H., 1997. Wrappers for feature subset selection, Artificial Intelligence 97 (1-2) 273-324Google Scholar

  • [33] Langley P., Iba W., Thompson K., 1992. An analysis of Bayesian Classifiers. In Proc. Of the 10th National Conf. on Artificial Intelligence, pages 223-228.Google Scholar

  • [34] Liu H., Setiono R., 1996. A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327Google Scholar

  • [35] Ma J., Kockelman KM., Damien P. 2008 A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention 40:964-975 (2008).CrossrefGoogle Scholar

  • [36] McCullagh P., Nelder J., 1989. Generalized Linear Models, London: Chapman and Hall, London, U.K.Google Scholar

  • [37] MCMVTAR 1976 Manual on Classification of Motor Vehicle Traffic Accidents-Revision of 016.11970, Third Edition, National Safety Council, Chicago, Illinois, 1976.Google Scholar

  • [38] Milton Jc, Shankar Vn, FL Mannering Fl Highway accident severities and the mixed logit model: An exploratory empirical analysis Accident Analysis & Prevention 40 (??), 260-266Google Scholar

  • [39] Molina L.C., Belanche L., and Nebot A., 2002. Feature Selection Algorithms: A survey and Experimental Evaluation. In Proc. Of the 2002 IEEE Intl. Conf. on Data Mining.Google Scholar

  • [40] Mujalli R.O., J. de Ona, 2012. Injury severity models for motor vehicle accidents: a review, Proceedings of the ICE - Transport, vol. 166, pp. 255-270.Google Scholar

  • [41] Mussone L., Ferrari A., Oneta M., 1999. An analysis of urban collisions using an artificial intelligence model, Accident Analysis and Prevention, vol. 31, pp. 705-718.Google Scholar

  • [42] Pearson, K., 1901. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, vol. 2, pp 559-572.CrossrefGoogle Scholar

  • [43] Popkin C.L., Campbell B.J. Hansen A.R., and Stewart J.R., 1991. Analysis of the accuracy of the existing KABCO injury scale, Chapel Hill, NC: University of North Carolina Highway Safety Research Center e-archives scan.Google Scholar

  • [44] Quddus M.A., Ison S.G., 2011. Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model, Accident Analysis & Prevention, vol. 43, pp. 1979-1990.Google Scholar

  • [45] Quinlan, J. R., 1986. Induction of Decision Trees. Machine Learning 1: 81-106, Kluwer Academic Publishers.CrossrefGoogle Scholar

  • [46] Quinlan, J. R.,1993 C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.Google Scholar

  • [47] Ramoni M., and Sebastiani P., 2001. Robust Bayes Classifier. Artificial Intelligence, 125: 209-226 Google Scholar

  • [48] Rezaie Moghaddam F., Afandizadeh Sh., Ziyadi M., 2011. Prediction of accident severity using artificial neural networks, International Journal of Civil Engineering, vol. 9,pp. 41-49.Google Scholar

  • [49] Rumelhart D.E., Hinton G.E.,Williams R. J., 1986. Learning internal representations by error propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations, Rumelhart D.E., McClelland J.L., and the PDP research group. (eds), MIT Press, 1986Google Scholar

  • [50] Savolainen, P.T., Mannering, F.L., Lord, D., and M.A. Quddus, 2011. “The Statistical Analysis of Highway Crash-Injury Severities: A Review and Assessment of Methodological Alternatives”, Accident Analysis and Prevention, Vol. 43, No. 5, 2011, pp. 1666-1676.Google Scholar

  • [51] Schwarz, Gideon E. 1978. Estimating the dimension of a model. Annals of Statistics 6 (2): 461-464CrossrefGoogle Scholar

  • [52] Shanthi S., Geetha Ramani, 2012. Feature relevance analysis and classification of road traffic accident data through data mining techniques, Proceedings of the World Congress on Engineering and Computer Science (WCECS 2012), October 24th-26th, 2012, San Francisco, U.S.A., Vol I, pp. 122-127.Google Scholar

  • [53] Shanti S., Geetha Ramani, 2012. Vehicle Safety Device (Airbag) Specific Classification of Road Traffic Accident Patterns through Data Mining Techniques. ACITY (2) 2012: 433-443Google Scholar

  • [54] Sohn, S.Y., Shin, H.W., 2001. Data mining for road traffic accident type classification, Ergonomics, vol. 44, pp. 107-117.Google Scholar

  • [55] Sohn S.Y., Lee S.H., 2003. Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea, Safety Science, vol. 41, pp. 1-14.Google Scholar

  • [56] Spearman C., 1904. General Intelligence, objectively determined and measured. Am J Psychol 15:202-93.Google Scholar

  • [57] Specht D. 1998. Probabilistic neural networks for classification, mapping, and associative memory, in Proceedings of the IEEE International Conference on Neural Networks, New York, U.S.A, pp. 525-532 (vol. 1).Google Scholar

  • [58] Tambouratzis Tatiana, Souliou Dora, Chalikias Miltiadis S., Gregoriades Andreas: Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction. IJCNN 2010: 1-8Google Scholar

  • [59] Tavakoli Kashani A., Shariat-Mohaymany A., Ranjbari A., 2012. Analysis of factors associated with traffic injury severity on rural roads in Iran, Journal of Injury and Violence Research, vol. 4, pp. 36-41Google Scholar

  • [60] Vilalta R., Drissi Y., A perspective view and survey of meta-learning, Artificial Intelligence Review, VOL. 18, PP. 77-95, 2002CrossrefGoogle Scholar

  • [61] Wang C., Quddus M.A., Ison S.G., 2011. Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model, Accident Analysis & Prevention, vol. 43, pp.1979-1990Google Scholar

  • [62] Worku Y.M., Deogratias E., Deo C., Maher Q. 2013, Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio. Accident Analysis & Prevention Volume 55, June 2013, Pages 202-210Google Scholar

  • [63] Zadeh, L.A., 1965. Fuzzy sets, Information and Control, vol. 8, pp. 338-353 Google Scholar

About the article

Published Online: 2014-12-30

Published in Print: 2014-01-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0023.

Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in