Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method
Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method
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DOI：
10.1049/cp.2012.1540
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Fangce Guo;R. Krishnan;J. Polak
中科院分区：
文献类型：
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作者：
Fangce Guo;R. Krishnan;J. Polak
短期交通预测是智能交通系统（ITS）研究的一个重要领域。许多 ITS 应用程序，例如高级旅行者信息系统 (ATIS)、动态路线引导 (DRG) 和城市交通控制 (UTC)，都可以从改进的短期未来交通变量预测中受益。异常情况（例如事件）期间的流量预测对于这些应用尤为重要。然而，这是一个尚未得到充分研究的领域。本文提出了基于奇异谱分析 (SSA) 技术的数据预处理对基于 k 最近邻 (kNN) 的流量预测器的新颖改进。该 SSA-kNN 框架用于正常和事故交通条件下的短期交通预测。这种方法的一个关键特征是数据预处理步骤，该步骤旨在适应事件条件下出现的极其嘈杂的传感器输入。本文将 SSA-kNN 方法与其他三种常用的机器学习方法 kNN、灰色系统模型 (GM) 和支持向量回归 (SVR) 的预测精度进行了比较。此外，还探讨了交通预测精度对各种 kNN 设计参数的敏感性。结果表明，所提出的基于 SSA-kNN 的方法在本研究中使用的方法中具有最佳的预测精度，特别是在非重复事件期间。该方法背后的概念可以扩展到其他机器学习工具，以提高短期交通预测模型的准确性。 （6页）
Short-term traffic prediction is an important area in Intelligent Transport Systems (ITS) research. A number of ITS applications such as Advanced Traveller Information Systems (ATIS), Dynamic Route Guidance (DRG) and Urban Traffic Control (UTC) can benefit from improved prediction of traffic variables for the short-term future. Traffic prediction during abnormal condition, such as incidents, is especially important to these applications. However, this is an area not well-researched. This paper presents a novel improvement to a k-Nearest Neighbour (kNN) based traffic predictor with Singular Spectrum Analysis (SSA) technique based data preprocessing. This SSA-kNN framework is implemented for short-term traffic prediction under both normal and incident traffic conditions. A key feature of this approach is the data pre-processing step, which is designed to accommodate the extremely noisy sensor inputs that arise during incident conditions. This paper compares the prediction accuracy of the SSA-kNN approach with three other commonly used machine learning methods, kNN, Grey System Model (GM) and Support Vector Regression (SVR). Moreover, the sensitivity of traffic prediction accuracy to various kNN design parameters is explored. The results show that the proposed SSA-kNN based approach has the best prediction accuracy among the methods used in this study, especially during non-recurring incidents. The concept behind the proposed method can be extended to other machine learning tools to improve the accuracy of short-term traffic forecasting models. (6 pages)