Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction
Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction
复制标题
DOI：
10.1016/j.engappai.2010.11.004
复制
发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
E. Mazloumi;G. Rose;G. Currie;S. Moridpour
中科院分区：
文献类型：
--
作者：
E. Mazloumi;G. Rose;G. Currie;S. Moridpour
神经网络已被用于众多的运输工程应用，因为它们具有强大的能力，复制现场数据的模式。预测总是受到来自两个来源的不确定性的影响：模型结构和训练数据。对于每个预测点，前者可以量化的置信区间，而总的预测不确定性可以通过构建一个预测区间。虽然置信区间是众所周知的运输工程背景下，很少有人注意到神经网络的预测区间的建设。本文提出的方法为构造神经网络的预测区间和量化每个不确定性来源对总预测不确定性的贡献程度提供了基础。应用所提出的方法来预测巴士旅行时间超过四个巴士路段在墨尔本，澳大利亚，导致总预测的不确定性的分量源的定量分解。总体而言，结果表明所提出的方法提供强大的预测区间的能力。
Neural networks have been employed in a multitude of transportation engineering applications because of their powerful capabilities to replicate patterns in field data. Predictions are always subject to uncertainty arising from two sources: model structure and training data. For each prediction point, the former can be quantified by a confidence interval, whereas total prediction uncertainty can be represented by constructing a prediction interval. While confidence intervals are well known in the transportation engineering context, very little attention has been paid to construction of prediction intervals for neural networks. The proposed methodology in this paper provides a foundation for constructing prediction intervals for neural networks and quantifying the extent that each source of uncertainty contributes to total prediction uncertainty. The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources. Overall, the results demonstrate the capability of the proposed method to provide robust prediction intervals.