PANGO: Prediction Model Based on Clustering of Time Series for Traffic Flow to Venues

With the increasing number of venues in the city, it provides better services for people. However, the ever-changing flow of people has also brought challenges to the management of venues, especially under the current regular prevention and control measures for COVID-19. Therefore, it is extremely necessary to propose a suitable prediction model for pedestrian volume to venues. The timing characteristics of venue’s traffic flow determine that the accuracy of the prediction results by applying classical prediction models is unsatisfactory. In order to resolve the problem, in this paper, a Long Short Term Memory network (LSTM) combined with clustering of time series named PANGO is proposed. In PANGO, the temporal clustering is proposed to solve the short-term dependence of traffic flow data, while the long-term cycle prediction model is applied to obtain the longterm cycle characteristics, so as to improve the accuracy of prediction. Finally, the results of multi-dimensional experiments show that the prediction accuracy of PANGO model is improved by 11.8% compared with the traditional LSTM model.