基于云计算的道路行程时间预测方法  

Method for Forecasting of Road Traveling Time Based on Cloud Computation

作　　者：夏淼 张国方[1] 宋景芬[1] Xia Miao;Zhang Guofang;Song Jingfen(Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学,湖北武汉430070

出　　处：《物流技术》2018年第8期55-58,62,共5页Logistics Technology

摘　　要：为了解决由于城市交通网络的负荷过大所导致的城市内部交通拥堵、交通事故频发等问题,对于基于云计算的道路行程时间的预测进行了研究,通过传感器采集道路交通的信息,通过无线网络将信息传送到云计算平台,通过云计算平台对信息进行分析,利用卡尔曼滤波算法完成道路行程时间的预测,利用预测结果可以对道路交通进行实时诱导,对于降低城市交通拥堵和交通事故发生的次数具有重要意义。In this paper, in order to solve the problems of the urban traffic network caused by overload, we studied the forecasting of road traveling time based on cloud computation. After analyzing the information of the road traffic collected using sensors and transmitted to the cloud computation platform through wireless network, we used the Kalman filter algorithm to forecast the road traveling time, using which,we can perform real-time guidance on the road traffic and reduce urban traffic congestion and the occurrence of traffic accidents.

关 键 词：云计算 智能交通 卡尔曼滤波 道路行程时间预测 

分 类 号：F253.9[经济管理—国民经济] F570