Passenger Flow Prediction Using Weather Data for Metro Systems

Abstract

Metro systems play an important role in reducing traffic congestion in large cities. In this paper, inspired by the potential impact of weather on passenger flow, we have developed an RNN-based model for metro passenger flow prediction with historical passenger flow data, the corresponding temporal data and weather data. A case study of passenger flow prediction model at Taipei Main Station is performed. The experimental results verify that adding the weather data to construct a passenger flow prediction model is contributory to improve the results.