Accesso libero

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

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Signals and Systems (special section, pp. 233-312), Ryszard Makowski and Jan Zarzycki (Eds.)
INFORMAZIONI SU QUESTO ARTICOLO

Cita

Ahmed, S.R. and Cook, A.R.(1982). Application of time-series analysis techniques to freeway incident detection, Transportation Research Record841: 19–21.Search in Google Scholar

Augugliaro, A., Dusonchet, L. and Sanseverino, E.R. (2002). An evolutionary parallel Tabu search approach for distribution systems reinforcement planning Advanced Engineering Informatics16(3): 205–215.10.1016/S1474-0346(02)00012-5Search in Google Scholar

Bortfeldt, A., Gehring, H. and Mack, D. (2003). A parallel tabu search algorithm for solving the container loading problem, Parallel Computing29(5): 641–662.10.1016/S0167-8191(03)00047-4Search in Google Scholar

Cao, L.J. and Tay, F.E.H. (2003). Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions on Neural Networks14(6): 1506–1518.10.1109/TNN.2003.820556Search in Google Scholar

Chen, S. and Wang, W. (2009). Decision tree learning for freeway automatic incident detection, Expert Systems with Applications36(2): 4101–4105.10.1016/j.eswa.2008.03.012Search in Google Scholar

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.10.1017/CBO9780511801389Search in Google Scholar

Dong, B., Cao, C. and Lee, S.E. (2005). Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings37(5): 545–553.10.1016/j.enbuild.2004.09.009Search in Google Scholar

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.Search in Google Scholar

Hagan, M.T., Demuth, H.B., and Beale, M. (1996). Neural Network Design, PWS, Boston, MA.Search in Google Scholar

Ho, S.C. and Haugland, D. (2004). A tabu search heuristic for the vehicle routing problem with time windows and split deliveries, Computers & Operations Research31(12): 1947–1964.10.1016/S0305-0548(03)00155-2Search in Google Scholar

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 Applications36(10): 12383–12391.10.1016/j.eswa.2009.04.047Search in Google Scholar

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 Science18(1): 75–83, DOI: 10.2478/v10006-008-0007-x.10.2478/v10006-008-0007-xSearch in Google Scholar

Jin, X., Cheu, R.L. and Srinivasan, D.(2002). Development and adaptation of constructive probabilistic neural network in freeway incident detection, Transportation Research10(2): 121–147.10.1016/S0968-090X(01)00007-9Search in Google Scholar

Lebrun, G., Charrier, C., Lezoray, O. and Cardot, H. (2008). Tabu search model selection for SVM Lebrun, International Journal of Neural Systems18(1): 19–31.10.1142/S012906570800134818344220Search in Google Scholar

Lin, J.Y., Cheng, C.T. and Chau, K.W. (2006). Using support vector machines for long-term discharge prediction, Hydrological Sciences Journal51(4): 599–612.10.1623/hysj.51.4.599Search in Google Scholar

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 Applications35(4): 1817–1824.10.1016/j.eswa.2007.08.088Search in Google Scholar

Lorena, A.C. and de Carvalho, A.C.P.L.F. (2008). Evolutionary tuning of SVM parameter values in multiclass problems, Neurocomputing71(16–18): 3326–3334.10.1016/j.neucom.2008.01.031Search in Google Scholar

Mahmoud, T.A. (2011). Adaptive control scheme based on the least squares support vector machine network, International Journal of Applied Mathematics and Computer Science21(4): 685–696, DOI: 10.2478/v10006-011-0054-6.10.2478/v10006-011-0054-6Search in Google Scholar

Pardo, M. and Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines, Sensors and Actuators B107(2): 730–737.10.1016/j.snb.2004.12.005Search in Google Scholar

Peter, T. (2013). Modeling nonlinear road traffic networks for junction control, International Journal of Applied Mathematics and Computer Science22(3): 723–732, DOI: 10.2478/v10006-012-0054-1.10.2478/v10006-012-0054-1Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Shawe-Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis, Cambridge University Press, New York, NY.10.1017/CBO9780511809682Search in Google Scholar

Srinivasana, D., Jin, X. and Cheu, R.L. (2005). Adaptive neural network models for automatic incident detection on freeways, Neurocomputing64: 473–496.10.1016/j.neucom.2004.12.001Search in Google Scholar

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 Science22(4): 841–854, DOI: 10.2478/v10006-012-0062-1.10.2478/v10006-012-0062-1Search in Google Scholar

Talbi, E.G., Hafidi, Z. and Geib, J.M. (1998). A parallel adaptive tabu search approach, Parallel Computing24(14): 2003–2019.10.1016/S0167-8191(98)00086-6Search in Google Scholar

Vapnik, V.N. (1999). An overview of statistical learning theory, IEEE Transactions on Neural Networks10(5): 988–999.10.1109/72.78864018252602Search in Google Scholar

Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer, New York, NY.10.1007/978-1-4757-3264-1Search in Google Scholar

Wei, C. and Wu, K. (1997). Developing intelligent freeway ramp metering control systems, National Science Council in Taiwan7C(3): 371–389.Search in Google Scholar

Wu, C.H., Ho, J.M. and Lee, D.T. (2004). Travel-time prediction with support vector regression, IEEE Transactions on Intelligent Transportation Systems5(4): 276–281.10.1109/TITS.2004.837813Search in Google Scholar

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).10.1016/j.trc.2013.12.005Search in Google Scholar

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 Systems3(6): 843–852.10.1080/18756891.2010.9727746Search in Google Scholar

Yu, B., William, H.K.L. and Mei, L.T. (2011). Bus arrival time prediction at bus stop with multiple routes, Transportation Research C19(6): 1157–1170.10.1016/j.trc.2011.01.003Search in Google Scholar

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 Transportation44(3):193-204.10.1002/atr.136Search in Google Scholar

Yu, B., Yang, Z.Z. and Li, S. (2012). Real-time partway deadheading strategy based on transit service reliability assessment, Transportation Research A46(8): 1265–1279.10.1016/j.tra.2012.05.009Search in Google Scholar

Yu, B., Yang, Z.Z. and Yao, B.Z. (2006). Bus arrival time prediction using support vector machines, Journal of Intelligent Transportation Systems10(4): 151–158.10.1080/15472450600981009Search in Google Scholar

Yuan F. and Cheu R.L. (2003). Incident detection using support vector machines, Transportation Research C11(3–4): 309–328.10.1016/S0968-090X(03)00020-2Search in Google Scholar

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 Applications37(9): 6618–6628.10.1016/j.eswa.2010.03.067Search in Google Scholar

eISSN:
2083-8492
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Mathematics, Applied Mathematics