Communications - Scientific Letters of the University of Zilina 2017, 19(4):36-42 | DOI: 10.26552/com.C.2017.4.36-42
Intelligent Modelling with Alternative Approach: Application of Advanced Artificial Intelligence into Traffic Management
- 1 Department of Management Theories, Faculty of Management Science and Informatics, University of Zilina, Slovakia
- 2 Department of Macro and Microeconomics, Faculty of Management Science and Informatics, University of Zilina, Slovakia
- 3 Research Institute of the IT4Innovations Centre of Excellence, Silesian University in Opava, Czech Republic
The currently existing transport infrastructures are failing due to many problems. This paper deals with presenting a new approach of modelling and forecasting transport processes using artificial intelligence. Firstly, the current state of forecasting transport data is presented; the traditional as well as new artificial intelligence methods, such as artificial neural networks, are discussed and described. After that, a support vector regression prediction model is briefly presented and an empirical analysis is performed. Finally, on the basis of our experiment and performed comparative analysis we state that artificial intelligence (AI) intelligent methods have potential in the transport area as they can improve the efficiency, safety, and environmental compatibility of transport systems
Keywords: forecasting; artificial intelligence; intelligent transport system; machine learning; support vector machines; upport vector regression; R software; statistical methods
Published: December 31, 2017 Show citation
ACS | AIP | APA | ASA | Harvard | Chicago | Chicago Notes | IEEE | ISO690 | MLA | NLM | Turabian | Vancouver |
References
- WOSYKA, J., PRIBYL, P.: Decision Trees as a Tool for Real-Time Travel Time Estimation on Highways. Communications-Scientific Letters of the University of Zilina. 15(2A), 11-16, 2013.
Go to original source...
- DIZO, J., BLATNICKY, M., SKOCILASOVA, B.: Computational Modelling of the Rail Vehicle Multibody System Including Flexible Bodies. Communications-Scientific Letters of the University of Zilina, 17(3), 2015.
Go to original source...
- SMITH, K., GUPTA, J.: Neural Networks in Business: Techniques and Application. Idea Group Publishing, Hershey, 2002.
- MILES, J. C., WALKER, A. J.: The Potential Application of Artificial Intelligence in Transport. Journal of Intelligent Transport Systems, 153(3), 183-198, 2006.
Go to original source...
- STENCL, M., LENDEL, V.: Application of Selected Artificial Intelligence Methods in Terms of Transport and Intelligent Transport Systems. Periodica Polytechnica Transportation Engineering, 40(1), 11-16, 2012.
Go to original source...
- LENDEL, V., STENCL, M.: Possibility of Applying Artificial Intelligence in Terms of Intelligent Transport Systems. Journal of Information, Control and Management systems, 8(1), 37-46, 2010.
- RAMOS, C., AUGUSTO, J. C., SHAPIRO, D.: Ambient Intelligence-The Next Step for Artificial Intelligence. Intelligent Systems, 23(2), 15-18, 2008.
Go to original source...
- SPALEK, J., JANOTA, A., BALAZOVICOVA, M., PRIBYL, P.: DECISION-MAKING and Management with the Support of Artificial Intelligence (in Slovak). EDIS, University of Zilina, p. 374, 2005.
- HRKUT, P.: Architecture of Providing Services in Intelligent Transportation Systems Using Semantically Defined Information. Communications-Scientific Letters of the University of Zilina, 12(3A), 75-79, 2010.
Go to original source...
- DICOVA, J., ONDRUS, J.: Trend of Public Mass Transport Indicators-as a Tool of Transport Management and Development of Regions. Communications-Scientific Letters of the University of Zilina, 12(3A), 121-126, 2010.
Go to original source...
- BROWN, R. G.: Statistical Forecasting for Inventory Control. McGraw-Hill, New York, 1959.
- BROWN, R. G.: Smoothing Forecasting and Prediction of Discrete Time Series. Englewood Cliffs. NJ, Prentice-Hall, 1963.
- KALMAN, R. E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME-Journal of Basic Engineering, 82(D), 35-45, 1960.
Go to original source...
- BOX, G. E. P., JENKINS, G. M.: Time Series Analysis: Forecasting and Control. CA: Holden-Day, San Francisco, 1976.
- ENGLE, R. F.: Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1008, 1982.
Go to original source...
- KECMAN, V.: Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, 2001.
- ANDERSON, J. A., ROSENFELD, E.: Neurocomputing: Foundations of Research: A Collection of Articles Summarizing the State-of-the-Art as of 1988. MIT Press, Cambridge, 1988.
Go to original source...
- HERTZ, J., KROGH, A., PALMER, R. G.: Introduction to the Theory of Neural Computation. Westview Press, Colorado, 1991.
Go to original source...
- RIVAS, V. M., MERELO, J. J., CASTILLO, P. A., ARENAS, M. G., CASTELLANO, J. G.: Evolving RBF Neural Networks for Time-Series Forecasting with EvRBF. Information Sciences, 165, 207-220, 2004.
Go to original source...
- HINTON, G. E., OSINDERO, S., TEH, Y.-W.: A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18, 1527-1554, 2006.
Go to original source...
- VAPNIK, V.: The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
Go to original source...
- VAPNIK, V., GOLOWICH, S. E., SMOLA, A.: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. Advances in Neural Information Processing Systems, 9, 281-287, 1996.
- CAO, L. J., TAY, F. E. H.: Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518, 2003.
Go to original source...
- CAO, L. J., TAY F. E. H.: Financial Forecasting Using Support Vector Machines. Neural Computing Applications; 10, 184-92, 2001.
Go to original source...
- FALAT, L., PANCIKOVA, L., HLINKOVA, M.: Prediction Model for High-Volatile Time Series Based on SVM Regression Approach. Proceedings of IEEE Information and Digital Technologies, Slovakia, 2015.
Go to original source...
- Support Vector Machines (SVM) Fundamentals Part-I. [online]. Available: https://panthimanshu17.wordpress.com/2013/07/28/svm-fundamentals-part-1/ (accessed 12.12.2014).
- CAO, D.-Z., PANG, S.-L., BAI, Y.-H.: Forecasting Exchange Rate Using Support Vector Machines. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, China, 2005.
- KIM, K.: Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing, 55, 307-319, 2003.
Go to original source...
- CRAWLEY, M.: The R Book. Wiley, Chichester, 2007.
Go to original source...
- CHANG, CH.-CH., Lin, CH.-J.: LIBSVM-A Library for Support Vector Machines [online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (accessed 22.12.2014).
- Misc Functions of the Department of Statistics (e1071) [online]. TU Wien. Available: http://cran.r-project.org/web/packages/e1071/e1071.pdf (accessed 10.12.2014).
- SVM Tutorial [online]. Available: http://www.svm-tutorial.com/2014/10/support-vector-regression-r/ (accessed 10.12.2014).
- R - manual [online]. Available: http://fria.fri.uniza.sk/~kmame/drupal/subory/pancikova/Rmanual/ (accessed 11.12.2014).
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.