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针对无法获得符合车载自组网特性的真实路网条件,通过修改CarAgent模块的诱导逻辑构建了一种典型的实时交通流诱导仿真场景,从而实现交通诱导信息与交通流运行之间的协同互反馈机制。利用此场景选择多种影响因素,从单变量方差分析和多因素二阶交互方差分析2个角度,分析交通诱导信息对车辆行程时间的影响。仿真结果表明:车辆诱导设备装载率、诱导信息发布有效距离和驾驶人对诱导信息的服从率与车辆行程时间之间存在显著的负相关关系。相比较而言,车辆诱导设备装载率对行程时间的影响程度最大,可减少行程时间约23.5%。
Aiming at the real road network conditions that can not meet the characteristics of in-vehicle ad hoc network, a typical real-time simulation model of traffic flow guidance is constructed by modifying the guidance logic of CarAgent module so as to achieve synergistic feedback between traffic guidance information and traffic flow mechanism. A variety of influencing factors were selected by using this scenario. From the perspectives of one-way ANOVA and two-way ANOVA, the influence of traffic-induced information on vehicle travel time was analyzed. The simulation results show that there is a significant negative correlation between the vehicle induction device loading rate, the effective distance of induced information release and the driver's compliance rate of induced information with the vehicle travel time. In contrast, vehicle-induced equipment loading rate of the maximum impact on the travel time, can reduce the travel time of about 23.5%.