Short Term Passenger Flow Forecast of Metro Based on Inbound Passenger Plow and Deep Learning

Abstract

Based on the suitable artificial intelligence model, this paper establishes the short-term passenger flow forecasting model of subway, studies the correlation characteristics that affect the short-term passenger flow forecasting of subway, and combines with the existing results, selects five related forecasting variables through data analysis, and uses copula correlation coefficient and Granger causality correlation analysis to process a large number of swipe card data. The weather factors, holidays, peak, the number of relevant stations and the number of outbound factors, are analyzed. By extracting the features that affect the accuracy of passenger flow forecast, using multiple constraints and taking the 15 minute interval as the time granularity, a passenger flow forecast method based on the combination of inbound passenger flow feature fusion and neural network prediction model (LSTM) is proposed. It not only improves the accuracy of passenger flow prediction, but also provides the basis for management decision-making, and provides reference information for people's travel.