A Stable Passenger Flow Forecast Approach for Newly Opened Metro Stations Based on Multi-Source Data and Random Forest Regression Model

Abstract

Forecasting the stable passenger flow of a metro station before its official operation can help the management department design a more efficient passenger flow organization scheme. However, the conventional methods based on four-step method of traffic planning have disadvantages such as a long forecasting period, low forecasting accuracy, and difficulty in obtaining data. On the other hand, because emerging big data forecasting methods are all driven by historical data, it is impossible to apply these methods before the official operation of stations. Therefore, this paper proposes a passenger flow forecast approach through multi-source data and random forest regression (RFR) model. These multi-source data mainly include the number of POI (Point of Interest), employment positions, and residents in the radiation area of the station, as well as the attributes and functions of the station. This paper obtained the multi-source data of 65 stations in Qingdao Metro for experimental evaluation. In the case study, this paper uses five models including random forest regression, linear regression, Bayesian ridge regression, elastic network, and gradient boosting regression for comparison. The results show that RFR has the most satisfactory accuracy. This study provides a new perspective for relevant management departments.