Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network

Area of Science:

• Transportation Science
• Operations Research
• Urban Planning

Background:

• Rail transit station closures are abnormal operational events impacting passenger mobility.
• Estimating the effects of these closures on passenger behavior and demand is crucial for network management.

Purpose of the Study:

• To develop a novel approach for estimating the impacts of alternative station closure scenarios on passenger behavior and demand.
• To construct a passenger behavior optimization model considering multi-modal transport and closure duration uncertainty.
• To create an integrated simulation-based algorithm for impact assessment.

Main Methods:

• Mathematical modeling using 0-1 integer programming for passenger behavior.
• Development of a passenger simulation algorithm for demand estimation.
• Numerical experiments using Beijing rail transit smart card data (2,074,267 records).

Main Results:

• The proposed behavior optimization model achieved approximately 80% accuracy compared to manual surveys.
• The model effectively captures passenger behavior and quantifies closure impacts on passenger flow.
• Closure duration and its overestimation significantly influence passenger choices and overall demand.

Conclusions:

• The developed model and algorithm provide a quantitative tool for assessing rail transit station closure impacts.
• Accurate estimation of closure duration is key to mitigating negative effects on passenger flow.
• Findings support improved operational planning and passenger information dissemination during disruptions.