监督交通量变化的多模型预测自适应交通信号灯控制

因信号设定时间和车流量动态行为引起的交通量变化是现代交通控制系统存在高度不确定性的主要因素.根据交通流量具有高峰期、正常期及突发超流量期的特点,本文提出了一种监督多模型交通流量建模方法,结合模型预测控制技术对交通信号灯进行优化式智能控制,对不同交通模式下交通流量的实时变化作出反应,在优化的模式下对关键主干道交叉路口的信号灯进行自适应调节,达到实现通行次数合理,车辆延误时间以及停车时间都减少的目的.仿真示例说明了该方法的有效性.

Intelligent traffic volume variation control with supervised multi-model traffic signal adaptive predictive control

A major issue in traffic control systems is the high level of uncertainty due to traffic volume variation in the dynamics of vehicular queue and signal timings. An approach is proposed to deal with the problem based on the supervised multi-model signal adaptive predictive control (SMM-SAPC). According to the characteristics in traffic flow, such as nominal period, peak period and a super flow period, a supervised multi-model approach for modeling the dynamic traffic flow is proposed. By incorporating the traffic modeling method within MPC with control traffic signal systems, a novel intelligent traffic control is implemented. Corresponding response will be made for different traffic conditions; an adaptive signal control for intersection in a main road can be implemented. The presented simulations are indicative for the reasonable traffic time and reduction in delay time and stop time that can be achieved by the proposed method.