Analysis of optimum time granularity selection in traffic prediction based on Pearson correlation coefficient

Abstract

An accurate passenger flow prediction is important for subway station operators and passengers, because it can reduce the congestion of subway stations, ensure passengers’ safety, and reduce passengers’ waiting time. To get an accurate prediction result, an appropriate time granularity selected in the prediction is necessary. The primary objective of this research is to find a time granularity as short as possible, and this time granularity can also ensure the stability and regularity of passenger flow. This paper analyzes the 25-day passenger flow data of 81 subway stations in Hangzhou, takes Pearson correlation coefficient as the index of the regularity, and analyzes the regularity of passenger flow in time granularity of five minutes, ten minutes, 15 minutes, 20 minutes, 25minutes, and 30 minutes under three different passenger flow modes on Saturday, Sunday and working day. The result shows that under the three different passenger flow modes, the passenger flow has strong regularity at all selected time granularities. Specifically, when the time granularity increases from five minutes to 15 minutes, the regularity increases significantly, and when the time granularity is greater than 15 minutes, the growth of regularity has significant slowdown.