Affiliations: Shandong Vocational College of Industry, Zibo, Shandong, China
Correspondence:
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Corresponding author: Liqiang Xu, Shandong Vocational College of Industry, Zibo, Shandong 255000, China. E-mail: [email protected].
Abstract: The research heat of artificial intelligence is increasing, and intelligent transportation is a direction of artificial intelligence. Short-term traffic flow prediction is the embodiment of use of artificial intelligence. In view of the problem that there is no communication between subgroups and the diversity of groups is limited after the convergence operation of mind evolutionary algorithm, this paper introduces learning mechanism and reflection mechanism to improve the mind evolutionary algorithm (RMEA). Through learning mechanism, each subgroup can obtain the winning individual information of all other subgroups on the premise of maintaining its own characteristics, and generating new individuals. After the learning mechanism, the reflection mechanism is used to select the best individuals, and the RMEA-WNN prediction model is constructed. Moreover, taking the prediction residual of model as the data set, the LSTM model is used to forecast the data of traffic flow residual error, and the RMEA-WNN-LSTM prediction model is constructed. The simulation prediction accuracy of the complex model reaches 96.8%, which proves that the model has practical application value.
