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首页> 外文期刊>Journal of Intelligent Transportation Systems >A Wavelet Network Model for Short-Term Traffic Volume Forecasting
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A Wavelet Network Model for Short-Term Traffic Volume Forecasting

机译：短期交通流量预测的小波网络模型

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摘要

Wavelet networks (WNs) are recently developed neural network models. WN models combine the strengths of discrete wavelet transform and neural network processing to achieve strong nonlinear approximation ability, and thus have been successfully applied to forecasting and function approximations. In this study, two WN models based on different mother wavelets are used for the first time for short-term traffic volume forecasting. The Levenberg-Marquardt algorithm is used to train the WN models because it has better efficiency than the other algorithms based on gradient descent. Using the traffic volume data collected on Interstate 80 in California, the WN models are compared with the widely used back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models. The performance evaluation is based on mean absolute percentage error (MAPE) and variance of absolute percentage error (VAPE). The test and comparison results show that the WN models consistently produce lower average MAPE and VAPE values than the BPNN and RBFNN models, suggesting that the WN models are a better predictor of accuracy, stability, and adaptability.
机译：小波网络（WN）是最近开发的神经网络模型。 WN模型结合了离散小波变换和神经网络处理的优势，实现了强大的非线性逼近能力，因此已成功应用于预测和函数逼近。在这项研究中，首次使用基于不同母小波的两个WN模型进行短期流量预测。 Levenberg-Marquardt算法用于训练WN模型，因为它比其他基于梯度下降的算法具有更高的效率。使用在加利福尼亚州80号州际公路上收集的交通量数据，将WN模型与广泛使用的反向传播神经网络（BPNN）和径向基函数神经网络（RBFNN）模型进行比较。性能评估基于平均绝对百分比误差（MAPE）和绝对百分比误差的方差（VAPE）。测试和比较结果表明，WN模型始终比BPNN和RBFNN模型产生更低的平均MAPE和VAPE值，这表明WN模型可以更好地预测准确性，稳定性和适应性。

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