A stopped time dependent randomization cellular automata model for traffic flow controlled by traffic light

Modelling road traffic behavior using cellular automata has become a well-established method to analyze, understand, and even forecast the behavior of real road traffic, because the automata's evolution rules are simple, computationally efficient. In this paper, we presented a new model. In this model, the randomization probability is defined to be function of the stopped time of the vehicle: the longer the vehicle stops, the larger the randomization probability is. This means that the sensitivity of the drivers depends on the stopped time. The simulations show that although the fundamental diagram of the new model is similar to that of Nagel–Schreckenberg model, the saturated current depends on the cycle time of traffic light. We have explained the dependence of saturated current on cycle time and made the outlook of the future work. Our results indicate that we can adjust the cycle time of the traffic lights to enhance the road capacity.