Passenger Flow Prediction and Risk Early Warning for Metro Cross-section based on Transportation Big Data

Abstract

With the increasing pace of urbanization and the diversification of urban transportation modes, the demand for urban transportation in people's life is also increasing. For the purpose of relieving urban traffic, cities have made great efforts to develop public transport, especially metro transport. This paper will use the subway swipe card data of Changsha city from March 1-15, 2023, and optimize the LSTM model based on particle swarm algorithm, to predict the metro cross-sectional passenger volume. Based on the data and methods, this paper investigates the short-time cross-sectional passenger flow forecasting of the metro using the variables of initial passenger flow, inbound passenger volume and outbound passenger volume as references. It is found that the combined short-time passenger flow prediction model (PSO-LSTM) has good prediction effect. Based on the standard LSTM model, the PSO algorithm optimizes the learning rate, the number of iterations and the number of hidden neurons in the LSTM parameters to construct the PSO-LSTM model. The video recognition technology more accurately reflects the traffic congestion and makes early warning. The short-time cross-sectional passenger flow prediction of subway will help to improve people's living standard and save transportation time cost, which is of practical significance to urban rail operating companies.