Search

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people's travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

With the rapid development of social economy, the traffic volume of urban roads has raised significantly, which has led to increasingly serious urban traffic congestion problems, and has caused much inconvenience to people's travel. By focusing on the complexity and long-term dependence of traffic flow sequences on urban road, this paper considered the traffic flow data and weather conditions of the road section comprehensively, and proposed a short-term traffic flow prediction model based on the attention mechanism and the 1DCNN-LSTM network. The model combined the time expansion of the CNN and the advantages of the long-term memory of the LSTM. First, the model employs 1DCNN network to extract the spatial features in the road traffic flow data. Second, the output spatial features are considered as the input of LSTM neural network to extract the time features in road traffic flow data, and the long-term dependence characteristics of LSTM neural network are adopted to improve the prediction accuracy of traffic flow. Next, the spatio-temporal characteristics of road traffic flow were regarded as the input of the regression prediction layer, and the prediction results corresponding to the current input were calculated. Finally, the attention mechanism was introduced on the LSTM side to give enough attention to the key information, so that the model can focus on learning more important data features, and further improve the prediction performance. The experimental results showed that the prediction effect of the 1DCNN-LSTM-Attention model under the weather factor was better than that without considering the weather factor. At the same time, compared with traditional neural network models, the prediction effect of the proposed model revealed faster convergence speed and higher prediction accuracy. It can be found that for short-term traffic flow prediction on urban roads, the 1DCNN-LSTM network structure considering the attention mechanism provides superior features. [ABSTRACT FROM AUTHOR]