Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Abstract

Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenge due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways' main lanes. The time resolution of the dataset was set to 15 minutes, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyzes for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during mid-day periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

Power communication digital flow prediction method based on VMD-LSTM-SVM model

“…LSTM is a special cyclic neural network proposed on the basis of RNN [7] . Compared with traditional cyclic neural networks, LSTM can well solve the problem of gradient vanishing and gradient explosion [8] . The power business flow sequence m-1 intrinsic mode component IMF after VMD decomposition and the LSTM prediction model are established.…”

Performance analysis of machine learning methods for short-term traffic prediction

“…Intelligent transportation system aims to improve the efficiency of transportation infrastructure, which needs driver early warning system and future traffic information required for various control decisions. This relies on advanced models that can accurately predict traffic parameters [3]. When it comes to traffic flow prediction, it aims to predict the number of vehicles passing through the designated road section within the agreed time, which is also known as traffic flow prediction [4].…”