Dynamic evolution of urban traffic based on improved Cellular Automata

Traffic congestion caused by traffic accidents has seriously affected daily life. The cellular automata model can predict traffic congestion after the traffic accident by simulating the characteristics of vehicle movement. However, the prediction accuracy is poor. Aiming at the shortcomings of the cellular automata model, we studied the characteristics of urban traffic flow, integrated the passenger car unit and random traffic flow. We also improved the probability optimization design in the traditional cellular automata model. Thus, an improved cellular automata model was put forward. The prediction accuracy of the improved model was higher and more stable than that of the traditional model. The model provided technical references for traffic congestion.

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