关键词: 智能交通, 短时交通流预测, 卷积神经网络, 门控循环单元, 时空特征

Abstract:

Aiming at the problem that existing traffic flow prediction models fail to make full use of the spatial and temporal characteristics of traffic flow data to achieve accurate prediction, a convolutional gated recurrent unit prediction model combined with the attention mechanism（ACGRU） is proposed. The model uses the Convolution Neural Network（CNN） and the Gated Recurrent Unit（GRU） to extract the spatiotemporal characteristics of traffic flowand then uses the attention mechanism to generate the traffic flow feature representation with the probability distribution of attention. At the same time, periodic characteristics of traffic flow are extracted by weekly similarity. All the features are integrated for regression prediction. Experiments on real traffic flow data sets show that the ACGRU model proposed in this paper has higher prediction accuracy and the prediction error is reduced by 9% on average compared with other prediction models.

Key words: intelligent transportation, short-term traffic flow forecast, Convolutional Neural Network（CNN）, Gated Recurrent Unit（GRU）, spatial-temporal feature