Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space-Time Analysis and GRU

• Published in: IEEE access Vol. 7; pp. 143025 - 143035
• Main Authors: Dai, Guowen, Ma, Changxi, Xu, Xuecai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Analytical models; Artificial neural networks; Correlation; Correlation analysis; Data models; Gated recurrent unit; Neural networks; Prediction algorithms; Prediction models; Predictive models; Roads; short-term traffic flow prediction; Spatial data; spatio-temporal analysis; traffic engineering; Traffic flow; Traffic information; Traffic models; Travel time; urban road section
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2941280

Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability.