A support vector machine with the tabu search algorithm for freeway incident detection

• Autorzy: Yao B. , Hu P. , Zhang M. , Jin M.

Identyfikatory

DOI

10.2478/amcs-2014-0030

• Języki publikacji: EN

Abstrakty

EN

Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

Słowa kluczowe

EN

automated incident detection; support vector machine (SVM); tabu search; freeway

PL

maszyna wektorów wspierających; odcinek swobodny trasy; algorytm tabu search

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: Vol. 24, no. 2
• Strony: 397--404
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Yao B.

• School of Automotive Engineering, Dalian University of Technology, Dalian 116024, PR China

autor

Hu P.

• School of Automotive Engineering, Dalian University of Technology, Dalian 116024, PR China

autor

Zhang M.

• School of Automotive Engineering, Dalian University of Technology, Dalian 116024, PR China

autor

Jin M.

• High Technology Research and Development Center, Ministry of Science and Technology, Beijing, PR China

Bibliografia

• [1] Ahmed, S.R. and Cook, A.R.(1982). Application of time-series analysis techniques to freeway incident detection, Transportation Research Record 841: 19–21.
• [2] Augugliaro, A., Dusonchet, L. and Sanseverino, E.R. (2002). An evolutionary parallel Tabu search approach for distribution systems reinforcement planning Advanced Engineering Informatics 16(3): 205–215.
• [3] Bortfeldt, A., Gehring, H. and Mack, D. (2003). A parallel tabu search algorithm for solving the container loading problem, Parallel Computing 29(5): 641–662.
• [4] Cao, L.J. and Tay, F.E.H. (2003). Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Networks 14(6): 1506–1518.
• [5] Chen, S. and Wang, W. (2009). Decision tree learning for freeway automatic incident detection, Expert Systems with Applications 36(2): 4101–4105.
• [6] Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, New York, NY.
• [7] Dong, B., Cao, C. and Lee, S.E. (2005). Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings 37(5): 545–553.
• [8] Falco, D., Del Balio, R., Tarantino, E. and Vaccaro, R. (1994). Improving search by incorporating evolution principles in parallel tabu search, IEEE Conference on Evolutionary Computation, Orlando, FL, USA, Vol. 2, pp. 823–828.
• [9] Hagan, M.T., Demuth, H.B., and Beale, M. (1996). Neural Network Design, PWS, Boston, MA.
• [10] Ho, S.C. and Haugland, D. (2004). A tabu search heuristic for the vehicle routing problem with time windows and split deliveries, Computers & Operations Research 31(12): 1947–1964.
• [11] Hou, S.M. and Li, Y.R. (2009). Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy, Expert Systems with Applications 36(10): 12383–12391.
• [12] Jeleń, L., Fevens, T. and Krzyżak, A. (2008). Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies, International Journal of Applied Mathematics and Computer Science 18(1): 75–83, DOI: 10.2478/v10006-008-0007-x.
• [13] Jin, X., Cheu, R.L. and Srinivasan, D.(2002). Development and adaptation of constructive probabilistic neural network in freeway incident detection, Transportation Research 10(2): 121–147.
• [14] Lebrun, G., Charrier, C., Lezoray, O. and Cardot, H. (2008). Tabu search model selection for SVM Lebrun, International Journal of Neural Systems 18(1): 19–31.
• [15] Lin, J.Y., Cheng, C.T. and Chau, K.W. (2006). Using support vector machines for long-term discharge prediction, Hydrological Sciences Journal 51(4): 599–612.
• [16] Lin, S.W., Ying, K.C., Chen, S.H. and Lee, Z.J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems with Applications 35(4): 1817–1824.
• [17] Lorena, A.C. and de Carvalho, A.C.P.L.F. (2008). Evolutionary tuning of SVM parameter values in multiclass problems, Neurocomputing 71(16–18): 3326–3334.
• [18] Mahmoud, T.A. (2011). Adaptive control scheme based on the least squares support vector machine network, International Journal of Applied Mathematics and Computer Science 21(4): 685–696, DOI: 10.2478/v10006-011-0054-6.
• [19] Pardo, M. and Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines, Sensors and Actuators B 107(2): 730–737.
• [20] Peter, T. (2013). Modeling nonlinear road traffic networks for junction control, International Journal of Applied Mathematics and Computer Science 22(3): 723–732, DOI: 10.2478/v10006-012-0054-1.
• [21] Ren, J.T., Ou, X.L., Zhang, Y., and Hu, D.C. (2002). Research on network level traffic pattern recognition, IEEE 5th International Conference on Intelligent Transportation Systems, Singapore, pp. 500–504.
• [22] Reyna, R., Giralt, A., and Esteve, D. (2001). Head detection inside vehicles with a modified SVM for safer airbags, IEEE 4th International Conference on Intelligent Transportation Systems, Oakland, MN, USA, pp. 500–504.
• [23] Shawe-Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis, Cambridge University Press, New York, NY.
• [24] Srinivasana, D., Jin, X. and Cheu, R.L. (2005). Adaptive neural network models for automatic incident detection on freeways, Neurocomputing 64: 473–496.
• [25] Sumi, S.M., Zaman, M.F. and Hirose, H. (2012). A rainfall forecasting method using machine learning models and its application to the Fukuoka city case, International Journal of Applied Mathematics and Computer Science 22(4): 841–854, DOI: 10.2478/v10006-012-0062-1.
• [26] Talbi, E.G., Hafidi, Z. and Geib, J.M. (1998). A parallel adaptive tabu search approach, Parallel Computing 24(14): 2003–2019.
• [27] Vapnik, V.N. (1999). An overview of statistical learning theory, IEEE Transactions on Neural Networks 10(5): 988–999.
• [28] Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer, New York, NY.
• [29] Wei, C. and Wu, K. (1997). Developing intelligent freeway ramp metering control systems, National Science Council in Taiwan 7C(3): 371–389.
• [30] Wu, C.H., Ho, J.M. and Lee, D.T. (2004). Travel-time prediction with support vector regression, IEEE Transactions on Intelligent Transportation Systems 5(4): 276–281.
• [31] Yao, B.Z., Hu, P., Lu X.H., Gao, J.J. and Zhang, M.H. (2013). Transit network design based on travel time reliability, Transportation Research C, DOI:10.1016/j.trc.2013.12.005, (in press).
• [32] Yao B.Z., Yang, C.Y., Yao, J.B. and Sun, J. (2010). Tunnel surrounding rock displacement prediction using support vector machine, International Journal of Computational Intelligence Systems 3(6): 843–852.
• [33] Yu, B., William, H.K.L. and Mei, L.T. (2011). Bus arrival time prediction at bus stop with multiple routes, Transportation Research C 19(6): 1157–1170.
• [34] Yu, B., Yang, Z.Z., Chen, K. and Yu, B. (2010). Hybrid model for prediction of bus arrival times at next station, Journal of Advanced Transportation 44(3):193-204.
• [35] Yu, B., Yang, Z.Z. and Li, S. (2012). Real-time partway deadheading strategy based on transit service reliability assessment, Transportation Research A 46(8): 1265–1279.
• [36] Yu, B., Yang, Z.Z. and Yao, B.Z. (2006). Bus arrival time prediction using support vector machines, Journal of Intelligent Transportation Systems 10(4): 151–158.
• [37] Yuan F. and Cheu R.L. (2003). Incident detection using support vector machines, Transportation Research C 11(3–4): 309–328.
• [38] Zhang, X.L., Chen, X.F. and He, Z.J. (2010). An ACO-based algorithm for parameter optimization of support vector machines, Expert Systems with Applications 37(9): 6618–6628.