Dynamic wavelet neural network model for traffic flow forecasting
Dynamic wavelet neural network model for traffic flow forecasting
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DOI：
10.1061/(asce)0733-947x(2005)131:10(771
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发表时间：
2005-10-01
影响因子：
--
通讯作者：
Adeli, H
中科院分区：
文献类型：
--
作者：
Jiang, XM;Adeli, H
在智能交通系统中，准确、及时的交通流预测对于有效管理交通拥堵至关重要。本文提出了一种用于交通流预测的非参数动态时滞递归小波神经网络模型。该模型结合了交通流中发现的自相似、奇异和分形特性。该模型引入并利用了小波框架的概念，为小波的设计提供了灵活性，并增加了交通流预测所需的自适应平移参数等额外特征。利用统计自相关函数选择交通流时间序列的最优输入维数。该模型结合了预测时间的时间和星期几。因此，除了短期预测外，它还可以用于长期交通流量预测。该模型已通过实际高速公路交通流数据进行了验证。该模型可以帮助交通工程师和高速公路机构制定有效的交通管理计划，以缓解高速公路拥堵。
Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. In this paper, a novel nonparametric dynamic time-delay recurrent wavelet neural network model is presented for forecasting traffic flow. The model incorporates the self-similar, singular, and fractal properties discovered in the traffic flow. The concept of wavelet frame is introduced and exploited in the model to provide flexibility in the design of wavelets and to add extra features such as adaptable translation parameters desirable in traffic flow forecasting. The statistical autocorrelation function is used for selection of the optimum input dimension of traffic flow time series. The model incorporates both the time of the day and the day of the week of the prediction time. As such, it can be used for long-term traffic flow forecasting in addition to short-term forecasting. The model has been validated using actual freeway traffic flow data. The model can assist traffic engineers and highway agencies to create effective traffic management plans for alleviating freeway congestions.