Abstract
With the development of big data, large-scale traffic flow forecasting which is a part of smart transportation has become an increasingly important research direction. Accurate and real-time traffic flow prediction is the key and difficult part of the traffic. The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle constitute a complex time correlation. For the current mainstream model, there are two main limitations: 1. Most of the existing models only focus on time correlation and ignore spatial correlation. 2. Even if the spatial correlation is concerned, the topological relationship between spaces is not fully considered. This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM). Specifically, PCA is used to reduce the dimension of data, GCN is used to learn the network topology of urban roads, LSTM is used to capture the time correlation of traffic flow. By comparing the results of different models, the proposed model is better than the current mainstream models.
Highlights
W ITH the development of hardware for data storage, a large amount of historical traffic flow information has been fully retained in the traffic information database of each city
This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM)
Based on the related research on the topological spatial correlation and temporal correlation of urban roads, this paper proposes a new method named PST-GCN (Principal Spatio-Temporal Graph Convolution Network), which is used for traffic prediction tasks based on urban road networks
Summary
W ITH the development of hardware for data storage, a large amount of historical traffic flow information has been fully retained in the traffic information database of each city. These dynamically changing traffic information often contain a large amount of rich information and regular pattern. By using historical data combined with big data intelligent algorithms, it can effectively predict future traffic flow and avoid traffic losses caused by uneven resource allocation. Through the use of big data for traffic prediction, various traffic modes can be fully explored, and future road traffic development trends can be deduced
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