Short-term Passenger Flow Forecast of Urban Subway Transportation Based on Deep Learning Methods

Abstract

With the acceleration of urban construction, many urban rail transits has entered the stage of network development. Facing the rail passenger flow with larger flow and more complex characteristics, how to forecast the rail passenger flow in real time and accurately has become an important topic in the field of transportation planning. Rail passenger flow is closely related to the travel patterns of urban residents, and is affected by factors such as weather conditions and station development Based on the heterogeneous data such as rail passenger flow data, POI data, meteorological data, air quality data and road network distribution data, this article analyzes the spatial and temporal distribution characteristics of rail passenger flow, conducts functional clustering analysis of stations, and constructs hybrid neural network using deep learning method to realize high-precision prediction of short-term passenger flow at rail stations.