Improving Short-Term Traffic Flow Prediction using Grey Relational Analysis for Data Filtering and Stacked LSTM Modeling

Abstract

Traffic flow prediction is one of the critical measures to alleviate traffic congestion. Currently, traffic flow prediction research has made some achievements, but there are still some deficiencies. In order to solve the problems of low prediction accuracy, poor real-time performance, and high data dimensions. This paper proposes a new traffic flow prediction method that employs Grey Relation Analysis (GRA) to detect the correlation between detection points, remove insignificant or uncorrelated traffic flow data points, and hence reduce the data dimensionality of the prediction model. Multiple Long Short-Term Memory (LSTM) models are then stacked to establish the traffic flow prediction model, considering that traffic flow is affected by multi-dimensional spatiotemporal factors, incorporating vehicle speed, occupancy, and traffic volume as inputs. We conducted experiments on real datasets, and the results showed that our GRA-SLSTM model improved prediction accuracy by 3.6% compared to other models, while reducing model prediction time by 27.33%. The proposed model’s generalization ability is validated by predicting other detection points, which provides significant references for traffic flow prediction research and practical applications.