Predicting Injury Severity Levels in Traffic Crashes: A Modeling Comparison
Publication: Journal of Transportation Engineering
Volume 130, Issue 2
Abstract
This paper investigates the use of two well-known artificial neural network (ANN) paradigms: the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks in analyzing driver injury severity. The objective of this study is to investigate the viability and potential benefits of using the ANN in predicting driver injury severity conditioned on the premise that a crash has occurred. The performance of the ANN was compared to a calibrated ordered probit model. Modeling results showed that the testing classification accuracy was 73.5% for the MLP, 70.6% for the fuzzy ARTMAP, and 61.7% for the ordered probit model. This result indicates a more accurate prediction capability of injury severity for ANN (particularly the MLP) over other traditional methods. The results of the models showed that gender, vehicle speed, seat belt use, type of vehicle, point of impact, and area type (rural versus urban) affect the likelihood of injury severity levels.
Get full access to this article
View all available purchase options and get full access to this article.
References
Al-Alawi, S., Ali, G., and Bakheit, C.(1996). “A novel approach for traffic accident analysis and prediction using artificial neural networks.” Road Transp. Res., 5, 118–128.
Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B.(1992). “Fuzzy ARTMAP: A neural-network architecture for incremental supervised learning of analog multidimensional maps.” IEEE Trans. Neural Netw., 3, 698–713.
Chang, L., and Mannering, F.(1999). “Analysis of injury severity andvehicle occupancy in truck- and non-truck-involved accidents.” Accid. Anal Prev., 31, 579–592.
Dietterich, T., Hild, H., and Bakiri, G.(1995). “A comparison of ID3 and back-propagation for English text-to speech mapping.” Mach. Learn., 18, 51–80.
Evans, L. (1991). Traffic safety and the driver, Van Nostrand Reinhold, New York.
Faghri, A., and Aneja, S.(1999). “Estimation of percentage of pass-by trips generated by a shopping center using artificial neural networks.” Transp. Plan. Technol., 22, 271–286.
Greene, W. (2000). Econometric analysis, 4th Ed., Prentice-Hall, Englewood Cliffs, N.J.
Grossberg, S.(1976). “Adaptive pattern recognition and universal recording. II: Feedback, expectation, olfaction, and illusions.” Biol. Cybern., 23, 187–202.
Haykin, S. (1999). Neural networks: A comprehensive foundation, Prentice-Hall, Englewood Cliffs, N.J.
Himanen, V., Nijkamp, P., and Reggiani, A. (1998). Neural networks in transport applications, Ashgate Publishing, Aldershot, U.K.
Khattak, A. and Cassidy, G. (1999). “Factors that influence multivehicle rear-end crashes: Analysis of crash propagation and injury severity.” Southern Transportation Center, Univ. of Tennessee, Knoxville, Tenn.
Kim, K., Nitz, L., Richardson, J., and Li, L.(1995). “Personal and behavioral predictors of automobile crash and injury severity.” Accid. Anal Prev., 27(4), 469–481.
Koufakou, A., Georgiopoulos, M., Anagnostopoulos, G., and Kasparis, T. (2001). “Cross-validation in fuzzy ARTMAP for large databases.” Neural Netw., in press.
Krull, K., Khattak, A., and Council, F. (2000). “Injury effects of rollovers and events sequence in single-vehicle crashes.” Transportation Research Record 1717, Transportation Research Board, Washington, D.C., 46–54.
Mussone, L., Ferrari, A., and Oneta, M.(1999). “An analysis of urbancollisions using an artificial intelligence model.” Accid. Anal Prev., 31, 705–718.
Nassar, S., Saccomanno, F., and Shortreed, J.(1994). “Road accidentseverity analysis: A micro level approach.” Can. J. Civ. Eng., 21, 847–855.
Rumelhart, D. E., and McClelland, J. L. (1986). Parallel distributed processing: Exploration in the microstructure of cognition. Volume 1: Foundations, MIT Press, Cambridge, Mass., 318–362.
Shankar, V., Mannering, F., and Barfield, W.(1996). “Statistical analysis of accident severity on rural freeways.” Accid. Anal Prev., 28(3), 391–401.
Snedecor, G., and Cochran, W. (1989). Statistical methods, 8th Ed., Iowa State University Press, Ames, Iowa.
Subba, R., Sikdar, P., Krishna, K., and Dhingra, S.(1998). “Another insight into artificial neural networks through behavioral analysis of access mode choice.” Comput. Environ. Urban Syst. 22(5), 485–496.
Tou, J., and Gonzalez, R. (1976). Pattern recognition principles, Addison-Wesley, Reading, Mass.
Information & Authors
Information
Published In
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Mar 18, 2002
Accepted: Apr 17, 2003
Published online: Feb 19, 2004
Published in print: Mar 2004
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.