集成技术2016，Vol.5Issue(6)：62-70,9.
基于 Spark和浮动出租车全球定位系统数据的实时交通路况预测方法
A Real-Time Trafifc Prediction Method Using Floating Taxi Global Positioning System Dataon Spark
摘要
Abstract
With the advance of urbanization and development of big data, urban trafifc forecast has become an essential issue for the Smart City. Many existing traffic prediction models do not fulfill the real-time performance goal in terms of efifciency and accuracy due to the limitation of hardware and software. A highly efficient real-time traffic prediction method using the Spark distributed in-memory computing framework was proposed in this paper. In this method, we estimate the average speed of vehicles on each road segment, which relfects the real-time trafifc condition. The method works in three steps. Firstly, we perform horizontal and vertical windowed sampling on historical GPS data. Secondly, we use Spark to compute the probability distribution of average speed over each time window. Thirdly, we use Bayesian maximum-a-posteriori estimation to adjust the speed estimate of latest period of time. Experimental results demonstrate that the proposed method can be used for implementing efifcient and accurate urban trafifc prediction in real time.关键词
实时路况预测/全球定位系统/Spark/北斗卫星导航系统Key words
real time trafifc prediction/GPS/Spark/Beidou navigation satellite system分类
矿业与冶金引用本文复制引用
程敏,张珣,白童心,须成忠..基于 Spark和浮动出租车全球定位系统数据的实时交通路况预测方法[J].集成技术,2016,5(6):62-70,9.基金项目
广东省自然基金项目(2014A030313687) （2014A030313687）
