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

Abstract

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.INDEX TERMS Short-time traffic flow prediction, variational modal decomposition, dung beetle optimization algorithm, long short term memory.

Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition

“…Research by numerous scholars has demonstrated significant improvements in model prediction accuracy following the application of Variational Mode Decomposition to time-series data. For instance, Zhao et al [31] initially smoothed traffic flow data using Variational Mode Decomposition, inputted the resulting subsequences into an LSTM network prediction model, optimized the model parameters using the dung beetle algorithm (IDBO), and aggregated the subsequences' predicted values to derive final predictions. Yu et al [32] applied Variational Mode Decomposition to segment historical traffic flow data into K components, determined by varying K sample entropy values.…”

Prediction of Tensile Properties in Inconel 625 Superalloy Fabricated by Wire Arc Additive Manufacturing Using Improved Artificial Neural Network

“…Since the DBO algorithm has a good balance between global search and local development, it has significant advantages in optimizing the BPNN. The existing research shows that using DBO to optimize the initial parameters of the BPNN can improve the convergence speed of the BPNN, reduce the training time, and obtain the optimal global solution [35,36]. Although the DBO-BPNN has been applied in engineering, there is still room for improvement in this method.…”

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

“…The final forecasting result is the sum of the results given by all the forecasters [13][14][15]. Hybrid models have shown performance superiority not only in the renewable energy forecasting field but also in other forecasting tasks and applications, such as wave forecasting [16][17][18], stock market index prediction [19] and traffic flow prediction [20].…”