Injury Severity Prediction of Traffic Collision by Applying a Series of Neural Networks: The City of London Case Study
This paper focuses on predicting injury severity of a driver or rider by applying multi-layer perceptron (MLP), support vector machine (SVM), and a hybrid MLP-SVM method. By correlating the injury severity results and the influences that support their creation, this study was able to determine the key influences affecting the injury severity. The result indicated that the vehicle type, vehicle manoeuvre, lack of necessary crossing facilities for cyclists, 1st point of impact, and junction actions had a greater effect on the likelihood of injury severity. Following this indication, by maximising the prediction accuracies, a comparison between the models was made through exerting the most sensitive predictors in order to evaluate the models’ performance against each other. The outcomes specified that the proposed hybrid model achieved a significant improvement in terms of prediction accuracy compared with other models.
Xie Y, Lord D, Zhang Y. Predicting Motor Vehicle Collisions using Bayesian Neural Networks: An Empirical Analysis. Accident Analysis & Prevention. 2007;39(5): 922-933.
DFT (Department for Transport). Instructions for the Completion of Road Accident Reports from non-CRASH Sources; 2011. Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/230596/stats20-2011.pdf [Accessed 28th June 2016].
Li Y, Ma D, Zhu M, Zeng Z, Wang Y. Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network. Accident Analysis & Prevention. 2018;111: 354-63.
Tabachnick BG, Fidell LS. Using multivariate statistics. 6th Edition. Boston, MA: Pearson; 2012.
Karlaftis MG, Vlahogianni EI. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies. 2011;19(3): 387-99.
Principe JC, Euliano NR, Lefebvre WC. Neural and adaptive systems: fundamentals through simulations. New York: Wiley; 2000.
Abdelwahab HT, Abdel-Aty MA. Artificial neural networks and logit models for traffic safety analysis of toll plazas. Transportation Research Record. 2002;1784(1): 115-25.
Li X, Lord D, Zhang Y, Xie Y. Predicting motor vehicle crashes using support vector machine models. Accident Analysis & Prevention. 2008;40(4): 1611-8.
Zhang HH. Variable selection for support vector machines via smoothing spline ANOVA. Statistica Sinica. 2006;16(2): 659-674.
Li Z, Liu P, Wang W, Xu C. Using support vector machine models for crash injury severity analysis. Accident Analysis & Prevention. 2012;45: 478-486.
Zhang J, Li Z, Pu Z, Xu C. Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learning and Statistical Methods. IEEE Access. 2018;6: 60079-60087.
Zeng Q, Huang H. A stable and optimized neural network model for crash injury severity prediction. Accident Analysis & Prevention. 2014;73: 351-358.
Abdelwahab HT, Abdel-Aty MA. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record. 2001;1746(1): 6-13.
Abdel-Aty MA, Abdelwahab HT. Predicting injury severity levels in traffic crashes: a modeling comparison. Journal of Transportation Engineering. 2004;130(2): 204-210.
Delen D, Sharda R, Bessonov M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis & Prevention. 2006;38(3): 434-44.
Yu R, Abdel-Aty M. Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety science. 2014;63: 50-56.
Chen C, Zhang G, Qian Z, Tarefder RA, Tian Z. Investigating driver injury severity patterns in rollover crashes using support vector machine models. Accident Analysis & Prevention. 2016;90: 128-139.
Iranitalab A, Khattak A. Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention. 2017;108: 27-36.
Chang LY, Wang HW. Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention. 2006;38(5): 1019-1027.
Burges CJ. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery. 1998;2(2): 121-167.
Vapnik VN. The nature of statistical learning theory. Springer; 1995.
Bellili A, Gilloux M, Gallinari P. An MLP-SVM combination architecture for offline handwritten digit recognition. Document Analysis and Recognition. 2003;5(4): 244-252.
Bishop CM. Neural networks for pattern recognition. Oxford University Press; 1995.
Curiel RP, Ramírez HG, Bishop SR. A novel rare event approach to measure the randomness and concentration of road accidents. PloS ONE. 2018;13(8): e0201890.
Siamidoudaran M, Iscioglu E, Siamidodaran M. Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: A case study of the city of London. SN Applied Sciences. 2019 Oct;1(10): 1268. Available from: doi:10.1007/s42452-019-1314-6
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