Improving traffic flow forecasting with relevance vector machine and a randomized controlled statistical testing
Improving traffic flow forecasting with relevance vector machine and a randomized controlled statistical testing
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DOI：
10.1007/s00500-018-03693-7
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发表时间：
2018-12
期刊：
影响因子：
4.1
通讯作者：
Jungang Lou;Zhangguo Shen;Qing Shen;Wenjun Hu;Zhijun Chen
中科院分区：
文献类型：
--
作者：
Jungang Lou;Zhangguo Shen;Qing Shen;Wenjun Hu;Zhijun Chen
高精度的交通流预测是智能城市交通系统发展的关键。近年来，基于核方法的交通流预测模型因其良好的泛化能力而得到了广泛的应用。本文的研究目的有两个：一是将一种新的核学习方法--相关向量机用于短时交通流预测，以捕捉序列交通流数据之间的内在相关性，它是一种精确的非线性模型，只需要少量的自动选择的相关基函数。因此，它可以找到简洁的数据表示，这是足够的学习任务保留尽可能多的信息。另一方面，学习样本的大小对预测精度有很大的影响。如何平衡样本容量与预测精度之间的关系是一个重要的研究课题。采用随机控制的统计检验方法，对新提出的交通流预测模型的样本容量进行了评价。实验结果表明，新模型的预测和泛化性能与已有模型相当或更好，且对学习样本的大小不敏感。
High-accuracy traffic flow forecasting is vital to the development of intelligent city transportation systems. Recently, traffic flow forecasting models based on the kernel method have been widely applied due to their great generalization capability. The aim of this article is twofold: A novel kernel learning method, relevance vector machine, is employed to short-term traffic flow forecasting so as to capture the inner correlation between sequential traffic flow data, it is a type of nonlinear model which is accurate and using only a small number of relevant basis functions automatically selected. So that it can find concise data representations which are adequate for the learning task retaining as much information as possible. On the other hand, the sample size for learning has a significant impact on forecasting accuracy. How to balancing the relationship between the sample size and the forecasting accuracy is an important research topic. A randomized controlled statistical testing is layout to evaluating the impacts of sample size of the new proposed traffic flow forecasting model. The experimental results show that the new model achieves similar or better forecasting and generalization performance compared to some old ones; besides, it is less sensitive to the size of learning sample.