Travel-time prediction with support vector regression
Travel-time prediction with support vector regression
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DOI：
10.1109/tits.2004.837813
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发表时间：
2004-12-01
影响因子：
8.5
通讯作者：
Lee, DT
中科院分区：
文献类型：
--
作者：
Wu, CH;Ho, JM;Lee, DT
旅行时间是交通运输中的一项基本指标。准确的出行时间预测对于智能交通系统和先进的出行者信息系统的发展也至关重要。本文将支持向量回归(SVR)应用于行程时间预测，并利用实际的公路交通数据将其结果与其他基线行程时间预测方法进行比较。由于支持向量机具有较强的泛化能力，并且对给定的训练数据保证全局最小，因此支持向量机在时间序列分析中具有很好的性能。与其他基线预报器相比，我们的结果表明，SVR预报器可以显著降低预测行程时间的相对平均误差和均方根误差。论证了支持向量机在行程时间预测中应用的可行性，证明了支持向量机在交通数据分析中的适用性和有效性。
Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.