The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.
Caggiani L, Dell’Orco M, Marinelli M, Ottomanelli M. A Metaheuristic Dynamic Traffic Assignment Model for O-D Matrix Estimation using Aggregate Data. Procedia - Social and Behavioral Sciences 2012;54:685-95.
Korzhenevych A, Dehnen N, Bröcker J, et al. Update of the Handbook on External Costs of Transport. Final Report. Ricardo-AEA/R/ ED57769. Issue Number 1; 2014.
Min W, Wynter L. Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies. 2011;19:606-16.
Chen F, Ding F, Alsaedi A, Hayat T. Data filtering based multi-innovation extended gradient method for controlled autoregressive moving average systems using the maximum likelihood principle. Mathematics and Computers in Simulation. 2017;132:53-67.
Rong Y, Zhang X, Feng X, Ho T-K, Wei W, Xu D. Comparative analysis for traffic flow forecasting models with real-life data in Beijing. Advances in Mechanical Engineering. 2015;7(12). doi: 10.1177/1687814015620324
Smith BL, Demetsky MJ. Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering. 1997;123:261-6.
Ledoux C. An urban traffic flow model integrating neural networks. Transportation Research Part C: Emerging Technologies. 1997;5:287-300.
Kumar K, Parida M, Katiyar VK. Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network. Procedia - Social and Behavioral Sciences 2013;104:755-64. doi: 10.1016/j.sbspro.2013.11.170
May RM. Biological Populations with Nonoverlapping Generations: Stable Points, Stable Cycles, and Chaos. Science. 1974;186:645-7.
Moloney JV, Newell AC. Nonlinear optics. Physica D: Nonlinear Phenomena. 1990;44:1-37.
Simmendinger C, Hess O. Controlling delay-induced chaotic behavior of a semiconductor laser with optical feedback. Physics Letters A. 1996;216:97-105.
Pecora L, Carroll T. Synchronization in chaotic systems. Physical Review Letters. 1990;64:821-4.
Short KM. Signal extraction from chaotic communications. International Journal of Bifurcation and Chaos. 1997;7(7):1579-1597.
Hussain K, Ismail F, Senu N. Solving directly special fourth-order ordinary differential equations using Runge–Kutta type method. Journal of Computational and Applied Mathematics. 2016;306:179-99. doi: 10.1016/j.cam.2016.04.002
Haworth B, Bruce E, Iveson K. Spatio-temporal analysis of graffiti occurrence in an inner-city urban environment. Applied Geography. 2013;38:53-63. doi: 10.1016/j.apgeog.2012.10.002
Díaz MH, Córdova FM, Cañete L, Palominos F, Cifuentes F, Sánchez C, et al. Order and Chaos in the Brain: Fractal Time Series Analysis of the EEG Activity During a Cognitive Problem Solving Task. Procedia Computer Science. 2015;55:1410-9. doi: 10.1016/j.procs.2015.07.135
Yuan X, Hwarng HB. Stability and chaos in demand-based pricing under social interactions. European Journal of Operational Research 2016;253:472–88. doi:10.1016/j.ejor.2016.02.047.
Çodur MY, Tortum A. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. Promet - Traffic & Transportation. 2015;27(3):217-25.
Park H, Joo S, Oh C. Development of an Evaluation Index for Identifying Freeway Traffic Safety Based on Integrating RWIS and VDS Data. Journal of Korean Society of Transportation. 2014;32:441-51.
Cohen L. Time-frequency Analysis. Prentice Hall PTR; 1995.
Gröchenig K. Foundations of Time-Frequency Analysis. Boston, MA: Birkhäuser Boston; 2001.
Kovacic DI, Brennan MJ. The Duffing Equation: Nonlinear Oscillators and their Behaviour. John Wiley & Sons; 2011.
Chacón R. Melnikov method approach to control of homoclinic/heteroclinic chaos by weak harmonic excitations. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences. 2006;364:2335-51.
Viana M, Oliveira K. Foundations of Ergodic Theory, Cambridge University Press; 2016.
Dutkay DE, Lai C-K. Spectral measures generated by arbitrary and random convolutions. Journal de Mathématiques Pures et Appliquées. 2016.
Kabirian A, Ólafsson S. Continuous optimization via simulation using Golden Region search. European Journal of Operational Research. 2011;208:19-27.
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