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首页> 外文期刊>IEEE transactions on industrial informatics >Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting
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Optimized Configuration of Exponential Smoothing and Extreme Learning Machine for Traffic Flow Forecasting

机译：用于交通流量预测的指数平滑和极限学习机的优化配置

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摘要

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods.
机译：交通流量预测是一种有用的技术，可用于解决交通拥堵问题和提高交通运输的机动性。与神经网络相关的方法已被用于开发交通预测模型已有二十多年了。由于神经网络在参数选择中非常敏感，因此选择适当的建模配置对于提高交通流量预测的准确性和效率至关重要。但是，这通常是通过反复试验的方法进行的，该方法非常耗时，同时涉及太多的设计因素。因此，本文利用一种健壮且系统的优化方法Taguchi方法来获得所提出的指数平滑和极限学习机预测模型的优化配置。将开发的模型应用于从英国高速公路和高速公路收集的真实世界数据，并将其与三个现有的预测模型进行比较。结果表明，Taguchi方法是有效的，并且能够进行预测模型的设计，所提出的优化配置模型在交通流量预测中具有出色的性能，在高峰和非高峰交通中高速公路和高速公路的准确率分别约为91％和88％期。

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