基于NS-BML模型的记忆密度在交通信号灯控制系统中的研究

raffic signal management and control directly affect the efficiency of traffic network. The NS-BML model is widely used in the simulation of traffic signal control system. For the problem of the current NS-BML model only considered the present instantaneous density but neglected the history density, the memory density strategy was proposed. This strategy was analyzed the influence of the proposed strategy to the efficiency of Manhattan network from two perspectives, the long memory density strategy and the short memory density strategy. Using time-discretization obtained the optimal proportion factor of the short-term memory density. The simulation results show that the efficiency of the system can be improved effectively by using the short memory density strategy proposed in this paper. At the same time, the efficiency of computer processing is guaranteed. The average speed and the rate of arrival of the system are increased by 8.51% and 9.28% respectively, indicating the effectiveness of the proposed strategy.