Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning
Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning
复制标题
短期交通流量预测： 时间序列分析与监督学习的实验比较
DOI：
10.1109/tits.2013.2247040
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发表时间：
2013-06-01
影响因子：
8.5
通讯作者：
Frasconi, Paolo
中科院分区：
文献类型：
--
作者：
Lippi, Marco;Bertini, Matteo;Frasconi, Paolo
近年来，交通流短期预测的研究取得了长足的发展。许多著作，描述了各种各样的不同的方法，往往共享类似的特点和想法，已出版。然而，介绍新预测算法的出版物通常采用不同的设置、数据集和性能测量，这使得难以推断出每种模型的优势和局限性的清晰图像。该文致力于研究两个问题.首先，我们回顾了概率图模型下的短期交通流预测方法，并进行了广泛的实验比较，为它们的性能分析提供了一个共同的基线，并提供了在公开数据集上操作的基础设施。其次，我们提出了两个新的支持向量回归模型，这两个模型是专门设计的，以受益于典型的交通流季节性，并显示了一个有趣的折衷预测精度和计算效率。与卡尔曼滤波器相结合的SARIMA模型是最精确的模型;然而，当在最拥挤的时期执行预测时，所提出的季节性支持向量回归机被证明是非常有竞争力的。
The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.