Prediction of urban interrupted traffic flow based on optimal convergence time interval

An urban discontinuous traffic flow prediction method based on optimal convergence time interval was proposed, aiming at the discontinuity, periodicity and randomness of urban traffic flow affected by signal control. Firstly, the signal control period of urban discontinuous traffic flow was obtained based on Fourier transform and autocorrelation analysis, and then cross validation mean square error model was used to determine the relationship between optimal convergence time interval and signal period. A LSTM_BConv prediction model combining Bayesian neural network and deep learning model was proposed based on the previous analysis. Experimental results show that: 1) Traffic flow data statistics based on optimal convergence time interval can effectively improve the prediction accuracy of urban discontinuous traffic flow prediction model; 2) The optimal convergence time interval of urban discontinuous traffic flow data is a multiple of the traffic signal control cycle; 3) Comparison test results show that LSTM_BConv model is superior to common benchmark models, and the average absolute percentage error is increased by 4.57%. The prediction results can provide reference for the optimization of signal control scheme. © 2023 Zhejiang University. All rights reserved.