Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model


Current urban hold up costs are increasing on account of the population growth of cities and increasing numbers of vehicles. Several cities are adopting intelligent transportation systems (ITSs) to boost traffic efficiency. ITSs will be used for monitoring tie up using detectors, such as calculating an estimated time of arrival or suggesting a detour route. In this paper, we propose an urban traffic flow prediction system employing a multifactor pattern recognition model, which combines Gaussian mixture model clustering with an artificial neural network. This method forecasts traffic flow by combining road geographical factors and environmental factors with traffic flow properties from ITS detectors. Experimental results demonstrate that the proposed model produces more reliable predictions compared with existing ways.

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