Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

To improve the accuracy of short term traffic flow prediction and to solve the problems of nonlinearity of short term traffic flow, more noise in the data, and more difficult to determine the parametes of long short term memory networks, a combined traffic flow prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization-long short term memory network (IDBO-LSTM) is proposed. First, to extract various modal components, the historical traffic flow data are smoothed using variational modal decomposition (VMD). Second, the LSTM prediction model is built for each individual subsequence, and the parameters of the LSTM are optimized using the IDBO algorithm which combines Singer chaos mapping, variable spiral search strategy, and Levy flight strategy. Finally, to acquire the final prediction results, the predicted values of various subsequences are added up and reassembled. Experiments were conducted using data collected from eight sensors along an interstate highway in California, and taking the straight road morning peak (S-M) data as an example, compared with LSTM and VMD-LSTM, the MAE of VMD-IDBO-LSTM is reduced by 26.69 and 7.5108, MAPE is reduced by 8.08059% and 2.27569%, and RMSE is reduced by 33.6912 and 8.7657. According to the findings, the VMD-IDBO-LSTM model that was proposed is capable of significantly improving the accuracy of short-term traffic flow prediction while also effectively addressing nonlinearity, data noise, and the difficulty of identifying the LSTM parameters.