Abstract
In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved a MAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abellán, J., López, G., de Oña, J.: Analysis of traffic accident severity using Decision Rules via Decision Trees. Expert Systems with Applications 40(15), 6047–6054 (2013)
de Oña, J., López, G., Mujalli, R., Calvo, F.: Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis & Prevention 51, 1–10 (2013)
Chang, L., Chien, J.: Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science 51(1), 17–22 (2013)
Fogue, M., Garrido, P., Martinez, F., Cano, J., Calafate, C., Manzoni, P.: A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms. Expert Systems with Applications 40(1), 323–336 (2013)
Commandeur, J., Bijleveld, F., Bergel-Hayat, R., Antoniou, C., Yannis, G., Papadimitriou, E.: On statistical inference in time series analysis of the evolution of road safety. Accident Analysis & Prevention 60, 424–434 (2013)
Weijermars, W., Wesemann, P.: Road safety forecasting and ex-ante evaluation of policy in the Netherlands. Transportation Research Part A: Policy and Practice 52, 64–72 (2013)
Antoniou, C., Yannis, G.: State-space based analysis and forecasting of macroscopic road safety trends in Greece. Accident Analysis & Prevention 60, 268–276 (2013)
García-Ferrer, A., de Juan, A., Poncela, P.: Forecasting traffic accidents using disaggregated data. International Journal of Forecasting 22(2), 203–222 (2006)
Quddus, M.: Time series count data models: An empirical application to traffic accidents. Accident Analysis & Prevention 40(5), 1732–1741 (2008)
Rojas, I., Pomares, H., Bernier, J.L., Ortega, J., Pino, B., Pelayo, F.J., Prieto, A.: Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing 42(1-4), 267–285 (2002)
Palmer, A., Montaño, J., Sesé, A.: Designing an artificial neural network for forecasting tourism time series. Tourism Management 27(5), 781–790 (2006)
Liu, F., Ng, G.S., Quek, C.: RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction. Neurocomputing 70(7-9), 1331–1341 (2007)
Roh, S.B., Oh, S.K., Pedrycz, W.: Design of fuzzy radial basis function-based polynomial neural networks. Fuzzy Sets and Systems 185(1), 15–37 (2011)
Gheyas, I.A., Smith, L.S.: A novel neural network ensemble architecture for time series forecasting. Neurocomputing 74(18), 3855–3864 (2011)
Yang, W., Tse, P.: Development of an advanced noise reduction method for vibration analysis based on singular value decomposition. NDT & E International 36(6), 419–432 (2003)
Reninger, P., Martelet, G., Deparis, J., Perrin, J., Chen, Y.: Singular value decomposition as a denoising tool for airborne time domain electromagnetic data. Journal of Applied Geophysics 75(2), 264–276 (2011)
Li, C., Park, S.: An efficient document classification model using an improved back propagation neural network and singular value decomposition. Expert Systems with Applications 36(2), 3208–3215 (2009)
Shih, Y., Chien, C., Chuang, C.: An adaptive parameterized block-based singular value decomposition for image de-noising and compression. Applied Mathematics and Computation 218(21), 10370–10385 (2012)
Kavaklioglu, K.: Robust electricity consumption modeling of Turkey using Singular Value Decomposition. International Journal of Electrical Power & Energy Systems 54, 268–276 (2014)
Al-Zaben, A., Al-Smadi, A.: Extraction of foetal ECG by combination of singular value decomposition and neuro-fuzzy inference system. Physics in Medicine and Biology 51(1), 137 (2006)
Yin, H., Zhu, A., Ding, F.: Model order determination using the Hankel matrix of impulse responses. Applied Mathematics Letters 24(5), 797–802 (2011)
Cong, F., Chen, J., Dong, G., Zhao, F.: Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis. Mechanical Systems and Signal Processing 34(1-2), 218–230 (2013)
Shores, T.S.: Applied Linear Algebra and Matrix Analysis, pp. 291–293. Springer, Heidelberg (2007)
Freeman, J.A., Skapura, D.M.: Neural Networks, Algorithms, Applications, and Programming Techniques. Addison-Wesley, California (1991)
Hagan, M.T., Demuth, H.B., Bealetitle, M.: Neural Network Design. Hagan Publishing, 12.19–12.27 (2002)
National Commission of Transit Security, http://www.conaset.cl/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Barba, L., Rodríguez, N. (2014). Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-13817-6_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13816-9
Online ISBN: 978-3-319-13817-6
eBook Packages: Computer ScienceComputer Science (R0)