A Map Reduce-Based Nearest Neighbor Approach for Big-Data-Driven Traffic Flow Prediction

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Abstract
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.
Funding Information
  • Science and Technology Foundation of Guizhou Province (LH20147386)
  • Fundamental Research Funds for the Central Universities (XDJK2016B016, XDJK2015B030, SWU114006)
  • Scientific Project of State Ethnic Affairs Commission of the People’s Republic of China (14GZZ012)
  • Natural Science Foundation of Chongqing CSTC (cstc2014jcyjA40016, cstc2015jcyjA40044)
  • National Natural Science Foundation of China (61403314, 61402380, 61528206)

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