Urban rail traffic passenger flow prediction method, apparatus, storage medium and computer device

Abstract

The invention relates to an urban rail traffic passenger flow prediction method, apparatus, a storage medium and a computer device. The method herein includes the following steps: real-time acquiring passengers' station entrance and exit data; on the basis of station entrance data and historical travel behaviors, predicting passengers' station exit places; based on the passengers' station entrance and exit data, computing passengers' travel routes by using the OD reverse algorithm, for passengers whose travel routes are not computed by using the OD reverse algorithm, using a multi-probability model to analyze passengers' station entrance and exit data so as to compute travel routes; computing passengers' computed travel routes in combination with passengers' station entrance places and exit places and real-time position information of trains to obtain passengers' real-time positions; on the basis of the real-time acquired passengers' station exit data, correcting passengers' real-time positions; and on the basis of passengers' corrected real-time positions, outputting predicted net passenger flow distribution information. According to the invention, the method herein can predict an individual passenger and makes a breakthrough to traditional modes which count statistics on entire passengers based on probability.

Description

Technical field [0001] The present invention relates to the technical field of intelligent transportation, in particular to a method, device, storage medium and computer equipment for predicting passenger flow of urban rail transit. Background technique [0002] Urban rail transit has attracted more and more passengers due to its large volume, low energy consumption, low pollution and convenient ride, and it has become the first choice of public transportation for people to travel. As more and more pedestrians choose to travel by subway, there is often an over-saturated passenger flow in subway stations, especially in the morning and evening peak hours or holidays. If the over-saturated passenger flow is not diverted in a timely and effective manner, it will easily lead to congestion, reduce the circulation rate of pedestrians, and may even lead to stampede accidents. Therefore, how to predict real-time passenger flow is particularly important. Current urban rail transit passen...

AI Technical Summary

Problems solved by technology

The current passenger flow distribution prediction of urban rail transit is mostly calculated based on group pr...

Images