作　　者： ; ; ; ;

机构地区： 吉林大学交通学院

出　　处： 《吉林大学学报（工学版）》 2010年第1期35-41,共7页

摘　　要： 针对SCATS信号控制系统存在的两个问题:相位差实时优化能力差和备选方案固化,尝试结合浮动车技术研究了SCATS自适应控制策略生成技术。通过改进地图匹配算法,提高浮动车路段行程时间估计精度,从而提高相位差实时优化能力以及模型的输入数据精度。同时根据实时的检测器数据和浮动车数据,采用模糊神经网络以及多属性决策技术自适应生成控制区域交叉口的配时参数。最后,以上海某SCATS控制区域为仿真路网,以SCATS采集的检测器数据及浮动车数据为仿真输入数据,以Paramics V6为仿真平台对本文模型进行验证,结果表明能够提高系统的控制效率。 Aiming at the two problems in the traffic signal control system of SCATS, poor real-time optimization capability for the phase-offset and having few options, the generation technique of the adaptive control strategy for the SCATS was investigated in combination with the probe vehicle data. The real-time phase-offset optimization capability and the accuracy of the input data of the model were improved through enhancing the accuracy of the probe vehicle link travel time evaluation by improvement of the map matching algorithm. Using a fuzzy neural network and a multiple attribute decision-making technique, the traffic signal control parameters were generated adaptively based on the real-time detector and probe vehicle data. The suggested model was validated using a SCATS control region of Shanghai city as an example of the simulation traffic network, the data sampled by the detectors of SCATS and the probe vehicles as the input of simulation, and the Paramics V6 as the simulation platform. The results showed that it can improve the SCATS signal control efficiency.

关 键 词： 交通运输系统工程 浮动车 交通控制 控制策略 模糊神经网络 多属性决策

领　　域： [交通运输工程] [交通运输工程]