The 20th and 21st joint COTA International Conference of Transportation Professionals (CICTP 2020-21)

Nowadays, the phenomenon of congestion in rail transit is becoming more and more severe which need effective passenger inducement measures to alleviate this phenomenon. For solving the problem of precise estimation of passengers in crowded conditions, this article studies from the basis of theories and focuses on interval full load rate. First, a model was designed according to interval full load rate and the number of passenger-controlled stations to calculate the rail traffic congestion degree. For getting the most accurate prediction results of the congestion degree, Autoregressive Integrated Moving Average model (ARIMA) and Prophet are compared to predict the interval full load rate. Then a simple model was designed to identify the Space-Time range of congestion based on full load rate. On the basis of the above theories, the Guangzhou Metro APP was taken as an example to realize precise induction functions.

CICTP