  Urban rail transit provides significant comprehensive benefits such as large
traffic volume and high speed, serving as one of the most important components
of urban traffic construction management and congestion solution. Using real
passenger flow data of an Asian subway system from April to June of 2018, this
work analyzes the space-time distribution of the passenger flow using
short-term traffic flow prediction. Stations are divided into four types for
passenger flow forecasting, and meteorological records are collected for the
same period. Then, machine learning methods with different inputs are applied
and multivariate regression is performed to evaluate the improvement effect of
each weather element on passenger flow forecasting of representative metro
stations on hourly basis. Our results show that by inputting weather variables
the precision of prediction on weekends enhanced while the performance on
weekdays only improved marginally, while the contribution of different elements
of weather differ. Also, different categories of stations are affected
differently by weather. This study provides a possible method to further
improve other prediction models, and attests to the promise of data-driven
analytics for optimization of short-term scheduling in transit management.

Machine Learning;Applications