Application of Artificial Intelligence Technology in Traffic Flow Forecast. Issue 2 (April 2021)

• Record Type: Journal Article
• Authors: Li, Yunxiang; Liu, Guochang; Cheng, Yingying; Wu, Jifei; Xiong, Yongyi; Ma, Ruosen; Wang, Yuhang
• Is Part Of: Journal of physics. Volume 1852:Issue 2(2021)
• Publication Date: 2021-04
• Subjects: Artificial Intelligence -- Deep Learning -- Short Traffic Flow Prediction -- LSTM; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1852/2/022076
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In recent years, China's urbanization process has been accelerating, the number of motor vehicles has been increasing, and the problems of traffic congestion, traffic noise and environmental pollution have become increasingly prominent. The application of artificial intelligence system in the field of transportation provides a good idea to solve the above problems. Traffic flow prediction is to estimate the traffic flow in a period of time according to historical traffic data. This technology can provide decision-making basis for traffic guidance and path planning. In this paper, the deep learning theory of artificial intelligence technology is used to predict the short-term traffic flow, and the LSTM prediction model is constructed. On the basis of preprocessing the original data, through correlation analysis, compression matrix construction and other steps, a more accurate short-term traffic flow prediction is realized. According to the model, this paper also discriminates the actual road congestion, and the prediction results are basically consistent with the actual situation.