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

随着大数据的发展和城市化进程的推进，城市交通路况预测成为智慧城市的焦点课题。而目前已有的实时路况预测模型由于软硬件的不足而不能进行准确高效的预测。文章利用真实的城市交通大数据，基于 Spark分布式内存计算框架，提出了一种高效的实时路况预测方法，其中实时路况用路段的平均速度体现。首先并行地对大量车辆的全球定位系统数据进行水平时间窗口和垂直时间窗口切片抽样，然后利用 Spark计算估测历史样本在各个时间段内历史平均速度的概率分布，最后采用贝叶斯最大后验估计基于新到的样本对未来的路况进行预测。实验结果表明，文章提出的方法可实现高效准确的实时路况预测。

A Real-Time Traffic Prediction Method Using Floating Taxi GlobalPositioning System Dataon Spark

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.