Exploring the impact of weather on Metro demand forecasting using machine learning method

TL;DR

This study investigates how weather variables influence metro passenger flow prediction accuracy using machine learning, revealing differential impacts on weekdays and weekends and across station types, thus aiding transit scheduling optimization.

Contribution

It introduces a multivariate regression approach incorporating weather data to enhance short-term metro demand forecasting accuracy.

Findings

Weather improves weekend prediction accuracy significantly.

Different weather elements impact passenger flow variably.

Station categories respond differently to weather influences.

Abstract

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.