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Short-term traffic flow rate forecasting based on identifying similar traffic patterns

机译：基于识别相似交通模式的短期交通流量预测

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摘要

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management. (C) 2015 Elsevier Ltd. All rights reserved.
机译：在交通管理和控制应用中，及时准确地预测交通发展的能力非常重要。本文提出了一种非参数和数据驱动的短期交通流量预测方法，该方法基于识别的流量模式，使用增强的K最近邻（K-NN）算法。加权欧几里得距离为最近的测量提供了更多权重，被用作K-NN的相似性度量。此外，实现邻居的白化来抑制主要候选者的影响，并且使用秩指数来聚合候选值。通过在从不同区域收集的大型数据集上实现该方法，并将其与高级时间序列模型（例如SARIMA和其他人提出的自适应卡尔曼滤波器模型）进行比较，证明了该方法的鲁棒性。结果表明，所提出的方法将平均绝对百分比误差降低了25％以上。此外，针对多个预测步骤评估了所提出的增强型K-NN算法的有效性，并在缺少值的数据下测试了其性能。这项研究提供了有力的证据，表明所提出的用于短期交通流量预测的非参数和数据驱动方法可提供有希望的结果。鉴于所提出方法的简单性，准确性和鲁棒性，可以将其轻松地与实时交通控制相结合，以进行主动式高速公路交通管理。 （C）2015 Elsevier Ltd.保留所有权利。

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