Traffic Safety Oriented Multi-Intersection Flow Prediction Based on Transformer and CNN. (15th February 2023)

• Record Type: Journal Article
• Authors: Fu, Tingting; Yu, Qianwen; Lao, Haksrun; Liu, Peng; Wan, Shaohua
• Other Names: Wang Licheng Academic Editor.
• Is Part Of: Security and communication networks. Volume 2023(2023)
• Publication Date: 2023-02-15
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2023/1363639
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Intelligent traffic signal control is one of the important means to ensure traffic safety. Effective signal control can make traffic flow fast and smooth, which first needs current and future traffic information. As the control of one intersection may affect adjacent intersections, this paper proposes to predict future traffic flow with consideration of multi-intersections. It can dynamically improve traffic conditions and reduce traffic congestion. Based on various nonlinear spatial relationships at correlated multi-intersections and the potential time-dependent relationship in traffic volume, a traffic flow prediction method named CNNformer which combines transformer with CNN, is proposed. The convolution neural network (CNN) and transformer are used to extract the spatial and temporal features of correlated multiple intersections. The learnable time code is embedded into transformer's location code, and the location information and time information are injected into the model to help it learn the time characteristics of traffic volume. This study also considers the impact of cyclical traffic flow pattern and proposes CNNformer + . In the method, all of the data from the previous time window, as well as the data from the prior week and month, are correspondingly entered into the network. Finally, the output is generated through average pooling, realizing the learning of cyclical traffic flow characteristics. In the experiment, CNNformer + and the state-of-the-art traffic flow prediction methods are compared using simulated data. The results show that the proposed model outperforms the existing models in prediction accuracy and efficiency.