Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model

Abstract

Real-time and accurate short-term traffic flow forecasting can provide important decision support for traffic guidance and management. To effectively address the spatial–temporal feature mining problem in short-term traffic flow prediction for complex road networks, a new method that combined the Pearson correlation coefficient (PCC) and improved genetic algorithm to optimize the long short-term memory model (IGA-LSTM) was constructed. It filters the traffic flow data of roads which are related to the spatial characteristics of the target road in the road network through the PCC model, and then the traffic flow data set was reconstructed. Secondly, the new traffic flow data set is treated as the input of the IGA-LSTM, so the PCC-IGA-LSTM forecasting model was proposed that can evaluate the influence of relevant roads on the target road. Finally, the performance of the model was evaluated with Seattle traffic flow data, and experiments of 5-min short-term traffic flow forecasting on both weekdays’ and weekends’ data sets verified the performance of the model respectively. Compared with the other forecasting models such as the IGA-LSTM, the PSO-BP the GA-BP model, and the proposed PCC-IGA-LSTM model, the experimental results show that the proposed model can better integrate the spatial–temporal correlation in the traffic flow data, and the accuracy is improved.

Keywords

• Pearson correlation coefficient (PCC)
• adaptive genetic algorithm
• long short-term memory model (LSTM)
• short-term traffic flow forecasting
• root mean square error (RMSE)
• spatial–temporal correlation