基于BP神经网络的物流交通实载率监测仿真  

Real load rate monitoring simulation of logistics traffic based on BP neural network

作　　者：刘瑜 李秋莎 LIU Yu;LI Qiu-sha(Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学,重庆400074

出　　处：《计算机仿真》2020年第6期383-387,共5页Computer Simulation

摘　　要：针对传统物流交通实载率监测方法仅处理静态信号,导致监测误差率较高,引用BP神经网络对物流交通实载率监测方法进行优化设计。在物流运输车辆和运输路线上,安装物流交通数据监测设备,为实载率的计算提供原始数据。借助监测设备对道路上运行的车辆进行识别,若识别为物流监测车辆,则利用BP神经网络对产生的监测信号进行动态处理。最终通过确定载重量、运输距离和车辆行驶里程的值,得出实载率的实时监测结果。通过仿真验证得出结论:设计的基于BP神经网络的物流交通实载率监测方法的平均误差率为1.4%,与传统方法相比降低了2.4%,且可准确监测实载率,保障了监测准确率。Due to high monitoring error rate in traditional method,this paper used BP neural network to optimize the monitoring method of actual load rate of logistics traffic.The logistics traffic data monitoring equipment was installed on the logistics transport vehicles and transportation paths,so as to provide raw data for the calculation of actual load rate.The vehicle running on the road was identified by monitoring devices.If it is identified as a logistics monitoring vehicle,the monitoring signal was dynamically processed by BP neural network.Finally,the real-time monitoring result of actual load rate was obtained by determining the values of load capacity,transportation distance and running mileage.Simulation results prove that the average error rate of designed method is 1.4%,which is 2.4%lower than the traditional method.meanwhile,this method can accurately monitor the actual load rate and ensure the monitoring accuracy rate.

关 键 词：BP神经网络 物流交通 交通实载率 物流监测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]