A Cellular Automaton Model with Random Update Rules for Urban Traffic Flow

Abstract

Traffic jamming can easily lead to wasting time and fuel consumption and induce traffic accidents, thus seriously affecting daily life. In this study, an urban traffic flow cellular automaton (CA) model with random update rules is proposed to analyze the influence of network size and the probabilities of the change of the motion directions of cars, from up to right (pur) and from right to up (pru) on traffic flow. Simulation results show that, as the size of the system increases, the critical density tends to decrease causing larger phase transition, and for a larger size network system, the critical density is stable. The greater the pur and pru, the greater the average velocity of vehicles, which means increase in the opportunity that vehicle change directions effectively avoids the formation of traffic jamming. By studying the operational status of urban traffic flow from the microlevel, it can provide some new ideas for alleviating urban traffic jamming.