Analyzing spatio-temporal impacts of extreme rainfall events on metro ridership characteristics

Abstract

Extreme rainfall events, such as heavy rainfall and typhoon, can cause unexpected disruptions to the metro ridership and operating system, resulting in severe consequences such as infrastructure malfunctions, service termination and system paralysis. This paper focuses on the spatio-temporal impacts and resilience assessment of extreme rainfall events on metro ridership. The ridership data used in this paper are from the Automatic Fare Collection (AFC) system in Shenzhen Metro, and the time ranges from May to September in 2017 and 2018 with the 15-minute granularity. This paper not only utilizes big data to analyze the spatio-temporal characteristics of passenger flow under heavy rainfall and typhoon, but also innovatively introduces the meteorological warning signals and ridership resilience curve to analyze the resilience of ridership. The main results reveal that the general heavy rainfall affects passenger flow in the spatio-temporal imbalance. Especially for the spatial aspect, the imbalance of direction and section in peak hours significantly aggravates and the section passenger volume is even larger than usual. For typhoon events, extreme weather can strongly affect the distributions and recovery of metro ridership. Stronger typhoons can have a greater impact on resilience, but continuous rainfall can lead to a longer recovery time. The study results can help metro management agencies better understand the impacts of extreme weather on metro ridership to build a more weather-resilience metro system.