Abstract
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.
Highlights
Traffic flow congestion has become a social predicament, which demands increasing national resources
In a chaotic state its shape is not smooth and stable; it shows irregular ups and downs, and the curve’s ascending speed is faster than the one in the periodic state. c) When the n value is the same, the L(k0) value in the large periodic state must be lower than the same values in the chaotic state
This study presents the importance of good and reliable chaotic pattern recognition in the traffic flow
Summary
Traffic flow congestion has become a social predicament, which demands increasing national resources. Road traffic flow prediction plays an important role for traffic managers and in planning the improvement of traffic flow management. Collecting data and using it to predict future traffic patterns in a way that enables the estimation of traffic flow congestion is of most importance. With the use of intelligent transport systems, traffic estimation and prediction has become more reliable for traffic managers [1]. Another predicament is the fact that some traffic congestions cannot be predicted in either space or time. The cause of the congestion of traffic flow is researched in various states of traffic flow
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