A Map Reduce-Based Nearest Neighbor Approach for Big-Data-Driven Traffic Flow Prediction
A Map Reduce-Based Nearest Neighbor Approach for Big-Data-Driven Traffic Flow Prediction
复制标题
DOI：
10.1109/access.2016.2570021
复制
发表时间：
2016
期刊：
影响因子：
3.9
通讯作者：
Dawen Xia;Huaqing Li;Binfeng Wang;Yantao Li;Zili Zhang
中科院分区：
文献类型：
--
作者：
Dawen Xia;Huaqing Li;Binfeng Wang;Yantao Li;Zili Zhang
在大数据驱动的交通流预测系统中，预测性能的稳健性取决于准确性和及时性。本文提出了一种新的基于MapReduce的最近邻（NN）方法，用于在Hadoop平台上使用相关分析（TFPC）进行交通流预测。特别是，我们开发了一个实时预测系统，包括两个关键模块，即，离线分布式训练（ODT）和在线并行预测（PART）。此外，我们建立了一个并行的$k$ -最近邻优化分类器，它将业务流之间的相关性信息的分类过程。最后，提出了一种新的预测计算方法，将实时观测到的交通流数据与ODT对大规模历史数据的分类结果相结合，生成真实的交通流预测。使用留一交叉验证方法对真实世界交通流大数据的实证研究表明，TFPC显著优于四种最先进的预测方法，即，自回归综合移动平均，朴素贝叶斯，多层感知器神经网络和NN回归，在准确性方面，在最好的情况下可以提高90.07%，平均绝对误差为5.53%。此外，它还显示了出色的加速、扩展和规模扩展。
In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel $k$ -nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup.