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摘要: 为了充分利用交通流的时空过程特性, 进行交通流的实时预测, 将过程神经元网络和数据流在线学习技术引入到短时交通流预测中。充分考虑交通流的日周期、周周期等内在特性, 结合过程神经元网络和小波变换, 实现对历史数据的多尺度过程特征处理。构建了路网整体预测过程神经元网络模型, 并采用主成分分析方法, 利用交通流空间相似性的影响对模型进行优化。基于Harr小波技术提出具有自适应和实时性预测特征的在线学习算法。试验结果表明: 该模型的预测准确性优于普通神经网络, 平均百分比相对误差降低6%~8%, 预测时间至少降低67%, 具有较高的性能, 能满足短时交通流实时预测的需求。Abstract: In order to fully utilize the spatio-temporal process characteristic of traffic flow and predict traffic flow in real time, both process neural network and the online learning technology of data stream were imported into short-term traffic prediction. Considering the inherent traffic features of daily-periodicity and weekly-periodicity, process neural network and wavelet transform were combined to deal with the multi-scale process characteristic of historical data. A road network prediction model was constructed, and was optimized by adopting principal component analysis and utilizing the influence of traffic flow space similarity. An online learning algorithm was proposed based on Hart wavelet technology, which has the characteristics of selfadaptability and real-time prediction. Experimental result shows that the forecasting accuracy of the model is better than ordinary neural networks, its relative error of mean percentage reduces by 6%-8%, and its prediction time reduces by 67% at least, so the model has good performance and can meet the demand of real-time prediction of short-term traffic flow.
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Key words:
- traffic prediction /
- short-time traffic flow /
- process neural network /
- wavelet transform
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