A support vector machine with the tabu search algorithm for freeway incident detection
By:
References
- Ahmed, S.R. and Cook, A.R.(1982). Application of time-series analysis techniques to freeway incident detection, Transportation Research Record841: 19–21.
- 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.
- Bortfeldt, A., Gehring, H. and Mack, D. (2003). A parallel tabu search algorithm for solving the container loading problem, Parallel Computing29(5): 641–662.
- 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.
- Chen, S. and Wang, W. (2009). Decision tree learning for freeway automatic incident detection, Expert Systems with Applications36(2): 4101–4105.
- 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.
- 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.
- 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.
- Hagan, M.T., Demuth, H.B., and Beale, M. (1996). Neural Network Design, PWS, Boston, MA.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Pardo, M. and Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines, Sensors and Actuators B107(2): 730–737.
- 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.
- 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.
- 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.
- Shawe-Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis, Cambridge University Press, New York, NY.
- Srinivasana, D., Jin, X. and Cheu, R.L. (2005). Adaptive neural network models for automatic incident detection on freeways, Neurocomputing64: 473–496.
- 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.
- Talbi, E.G., Hafidi, Z. and Geib, J.M. (1998). A parallel adaptive tabu search approach, Parallel Computing24(14): 2003–2019.
- Vapnik, V.N. (1999). An overview of statistical learning theory, IEEE Transactions on Neural Networks10(5): 988–999.
- Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer, New York, NY.
- Wei, C. and Wu, K. (1997). Developing intelligent freeway ramp metering control systems, National Science Council in Taiwan7C(3): 371–389.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- Yuan F. and Cheu R.L. (2003). Incident detection using support vector machines, Transportation Research C11(3–4): 309–328.
- 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.
Language: English
Page range: 397 - 404
Submitted on: Jul 18, 2013
Published on: Jun 26, 2014
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2014 Baozhen Yao, Ping Hu, Mingheng Zhang, Maoqing Jin, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.