TRAFFIC FLOW PREDICTION USING HYBRID DECOMPOSITION MODELS

Abstract

Traffic congestion is a significant issue for all social classes in society. The nation's economy is impacted by traffic congestion either directly or indirectly. The convenience of road users is necessary to guarantee the nation’s economic progress. Traffic prediction is necessary to address this issue since it allows us to estimate or predict future traffic. These days, one of the most significant and well-known growing fields is machine learning (ML), which is a subfield of artificial intelligence (AI). Machine learning has emerged as a crucial and promising study field in transportation engineering, particularly in the area of traffic forecasting but the traditional models use shallow networks for prediction. Now, deep architecture models such as Deep learning, a kind of machine learning technique, has recently captured both academic and commercial attention but it has limitations for an accurate traffic flow prediction as the model performance can be impaired by external factors like weather conditions, which made the researchers to switch to hybrid models for better prediction. A decomposition method for traffic flow can lessen the effects of noise and increase the accuracy of predictions. In this study, Multivariate Empirical Mode Decomposition (MEMD) was integrated along with different Machine Learning and Deep Learning techniques to predict traffic flow. In order to test its applicability in real-field scenarios, a real-time field data was also used. When compared to standalone models, the hybrid models, notably MEMD-LSTM and MEMD-RF, performed better. When used with the PeMS dataset, these hybrid models showed lower errors and higher determination coefficients of 0.925 and 0.820, respectively. Similar to this, MEMD-LSTM and MEMD ANN demonstrated improved determination coefficients of 0.891 and 0.878, respectively, when tested on the real-field dataset. This study addresses a gap in the literature by examining how well the proposed hybrid decomposition model predicts traffic flow utilising hybrid models