Dynamics of in-station time within metro systems: Measurement and determining factors

Worldwide, people living in mega cities are increasingly dependent on metro systems for their daily commutes, with approximately 168 million passengers regularly using metro networks across 182 cities in 56 countries, resulting in a global annual ridership of 54 billion through metro stations. Despite their importance, the travel experience of metro users has not been closely examined. Travel time estimates often overlook in-station time (IST), which can be substantial, especially at large interchange stations with multiple exits and platforms. This omission significantly impacts the overall travel experience.

Professor Becky P.Y. Loo and PhD candidate Hui Wang conducted novel research to measure in-station time dynamics systematically. To capture the complexity of individual behavior and interactions, they used an agent-based modeling approach. This approach includes passenger agents, train agents, and control center agents. A robust quantile regression model was then built to capture the variability of in-station time and analyze the underlying factors. The research makes a methodological contribution by developing an agent-based model that takes into account the total passenger experience in relation to station design and layout, train schedules, operations management, and passenger characteristics such as total volume, walking speed, trip origins, trip destinations, and their interactions. Additionally, the methodological framework offers exceptional flexibility in parameter and scenario setting, making it applicable to any large underground transport or non-transport infrastructure that requires enhancement of travel experiences.

For further visual insights into this research, we encourage you to view the accompanying video.