The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework
The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework
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DOI：
10.1080/15472450.2017.1283989
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发表时间：
2017-01-01
影响因子：
3.6
通讯作者：
Polak, John W.
中科院分区：
文献类型：
--
作者：
Guo, Fangce;Krishnan, Rajesh;Polak, John W.
本文研究了替代数据平滑和交通预测方法对两阶段短期城市出行时间预测框架性能准确性的影响。使用这个框架，我们测试了两种不同的数据平滑和四种不同的预测方法，使用行程时间数据从两个基本不同的城市交通环境，在正常和异常条件下的组合的影响。这构成了迄今为止对平滑和预测因子选择的联合影响的最全面的实证评估。结果表明，使用数据平滑提高预测精度，无论使用的预测方法，这是真实的，在不同的交通环境中，在正常和异常（事件）的条件。此外，数据平滑的使用通常比特定预测方法的选择对预测性能的影响大得多，并且这与所使用的特定平滑方法无关。在正常的交通条件下，不同的预测方法产生大致相似的结果，但在异常条件下，懒惰的学习方法成为上级。
This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior.