Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from ${Xi’an}$ city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods.


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