摘要
针对交通系统的动态性和随机性,提出了一种信号交叉口的自适应控制模型。设置主相位优先闽值,主相位具有优先权。采用RBF网络预测短时交通流信息,评估模块对可选相位的交通需求度进行评估。决策模块根据相位博瘁选择机制,选取后续放行相位;根据各相位的交通强度由模糊推理得到当前相位的绿灯延长时间。车辆平均延误作为评价指标,仿真结果表明,系统控制效果明显优于传统控制,在交通环境突然变化时控制效果更佳。
On account of the random,dynamic fluctuation of traffic demands,an adaptive control model of signalized intersection was proposed.Rational priority threshold was set,and the major phase has a higher degree of priority than minor one.RBF neural network was used to forecast short-term traffic flow.The evaluation module was used to calculate the traffic urgency degree of all lanes,and the decision module was used to select the next phase among the possible ones based on game.Extension of the current green phase was decided through fuzzy inference according to traffic intensity in each phase.Take the average delay of vehicles as the performance criterion,the simulation shows that this control method is obviously better than traditional ones,especially for the uncertain change of the traffic condition.
出处
《计算机仿真》
CSCD
北大核心
2013年第7期151-155,共5页
Computer Simulation
基金
山西省高校科技研究开发项目(20111126)
关键词
交通信号
博弈
模糊
Traffic signal
Game
Fuzzy
