1. bookVolumen 4 (2014): Edición 1 (January 2014)
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2449-6499
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30 Dec 2014
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Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Publicado en línea: 30 Dec 2014
Volumen & Edición: Volumen 4 (2014) - Edición 1 (January 2014)
Páginas: 31 - 42
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
Idiomas
Inglés

[1] AAAM, 1985. Abbreviated Injury Scale 1985. Des Plaines IL: Association for the Advancement of Automotive Medicine.Search in 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. 204-210.10.1061/(ASCE)0733-947X(2004)130:2(204)Search in 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. 6-13.10.3141/1746-02Search in Google Scholar

[4] Akaike, H., 1974. A new look at the statistical model identification, IEEE Transactions on Automatic Control 19(6): 716-72310.1109/TAC.1974.1100705Search in Google Scholar

[5] Akaike Hirotugu, 1980. Likelihood and the Bayes procedure, in Bernardo, J. M.; et al., Bayesian Statistics, Valencia: University Press, pp. 143-166.Search in 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. 187-196.10.1097/00005373-197403000-00001Search in 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. 463-468.10.1109/iNCoS.2012.75Search in 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.Search in Google Scholar

[9] Breiman Leo, 1996. Bagging predictors. Machine Learning 24(2): 123-140.10.1007/BF00058655Search in Google Scholar

[10] Breiman, Leo., 1998 Arcing classifiers, The Annals of Statistics, vol. 26, pp.801-849.10.1214/aos/1024691079Search in Google Scholar

[11] Breiman Leo., 2001 Random Forests. Machine Learning, volume 45, pp.5-32.10.1023/A:1010933404324Search in Google Scholar

[12] Catell, R.B., 1966. The scree test for the number of factors. Multivariate Behavioral Research, 1,245-27610.1207/s15327906mbr0102_1026828106Search in Google 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. 1019-1027.10.1016/j.aap.2006.04.00916735022Search in 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. 17-22.10.1016/j.ssci.2012.06.017Search in 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.Search in 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. 434-444.10.1016/j.aap.2005.06.02416337137Search in Google Scholar

[17] Devijver P.A., Kittler J. 1982. Pattern Recognition: A Statistical Approach, Prentice-Hall, London, U.K.Search in 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)10.1016/j.ejor.2004.08.010Search in Google Scholar

[19] Fisher, R. A., 1936. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7 (2): 179-188.10.1111/j.1469-1809.1936.tb02137.xSearch in Google Scholar

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

[22] Glover F., 1977. Heuristics for integer programming using surrogate constraints. Decision Sciences, vol. 8, pp. 156-166.10.1111/j.1540-5915.1977.tb01074.xSearch in Google Scholar

[23] Goodman, SN 1999. Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of Internal Medicine 130: 995-100410.7326/0003-4819-130-12-199906150-0000810383371Search in Google Scholar

[24] Grossberg S., 1987. Competitive learning: from interactive activation to adaptive resonance, Cognitive Science, vol. 11, pp. 23-63.10.1111/j.1551-6708.1987.tb00862.xSearch in Google Scholar

[25] Hall M. A., 1998. Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New ZealandSearch in Google 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 1.10.1145/1656274.1656278Search in Google Scholar

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

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

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

[30] Kaiser, H. F., 1960 The application of electronic computer to factor analysis. Educational and Psychological Measurement, 20, 141-151.10.1177/001316446002000116Search in 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. 167-177.10.3141/1840-19Search in Google Scholar

[32] Kohavi R., John G. H., 1997. Wrappers for feature subset selection, Artificial Intelligence 97 (1-2) 273-32410.1016/S0004-3702(97)00043-XSearch in Google 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.Search in Google Scholar

[34] Liu H., Setiono R., 1996. A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327Search in Google 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).10.1016/j.aap.2007.11.002Search in Google Scholar

[36] McCullagh P., Nelder J., 1989. Generalized Linear Models, London: Chapman and Hall, London, U.K.10.1007/978-1-4899-3242-6Search in 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.Search in 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 (??), 260-26610.1016/j.aap.2007.06.006Search in Google 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.Search in 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. 255-270.10.1680/tran.11.00026Search in 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. 705-718.10.1016/S0001-4575(99)00031-7Search in Google Scholar

[42] Pearson, K., 1901. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, vol. 2, pp 559-572.10.1080/14786440109462720Search in Google 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.Search in 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. 1979-1990.10.1016/j.aap.2011.05.016Search in Google Scholar

[45] Quinlan, J. R., 1986. Induction of Decision Trees. Machine Learning 1: 81-106, Kluwer Academic Publishers.10.1007/BF00116251Search in Google Scholar

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

[47] Ramoni M., and Sebastiani P., 2001. Robust Bayes Classifier. Artificial Intelligence, 125: 209-226 10.1016/S0004-3702(00)00085-0Search in 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.Search in 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, 1986Search in Google 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. 1666-1676.10.1016/j.aap.2011.03.025Search in Google Scholar

[51] Schwarz, Gideon E. 1978. Estimating the dimension of a model. Annals of Statistics 6 (2): 461-46410.1214/aos/1176344136Search in Google 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.Search in 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: 433-44310.1007/978-3-642-31552-7_45Search in Google Scholar

[54] Sohn, S.Y., Shin, H.W., 2001. Data mining for road traffic accident type classification, Ergonomics, vol. 44, pp. 107-117.10.1080/00140130120928Search in 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. 1-14.10.1016/S0925-7535(01)00032-7Search in Google Scholar

[56] Spearman C., 1904. General Intelligence, objectively determined and measured. Am J Psychol 15:202-93.10.2307/1412107Search in 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).Search in 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: 1-810.1109/IJCNN.2010.5596610Search in Google 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. 36-4110.5249/jivr.v4i1.67329127921502788Search in Google Scholar

[60] Vilalta R., Drissi Y., A perspective view and survey of meta-learning, Artificial Intelligence Review, VOL. 18, PP. 77-95, 200210.1023/A:1019956318069Search in Google 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.1979-199010.1016/j.aap.2011.05.01621819826Search in Google 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 202-21010.1016/j.aap.2013.03.008Search in Google Scholar

[63] Zadeh, L.A., 1965. Fuzzy sets, Information and Control, vol. 8, pp. 338-353 10.1016/S0019-9958(65)90241-XSearch in Google Scholar

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