Researchers are using rider data to better understand how stressed subway crowds flow through the system.

London's Tube system works a bit like the network of blood vessels in the human body. One closed station or delayed train can throttle movement across the entire system, like a blockage in an artery. Tube delays and emergency closures are a fact of life for  London, but they don't have to be as debilitating as a heart attack. By looking at ridership data to see where and how Underground users move throughout the system, researchers are finding new ways of dealing with increased congestion during station closures, and even developing strategies to keep the flow moving, like the blood of a healthy person.

Researchers at University College London have teamed up with the Underground's operator, Transport for London, to analyze system usage information for millions of riders. Based on data collected at stations through the Oyster card transit pass system, the researchers were able to track and analyze about 4 million movements per day.

By looking at all this data, they've been able to verify the assumption that there are certain major stations in the system that are used the most, but also to understand how they interrelate. Blockages or delays at one of the major station can affect how ridership flows at the other major stations, for example. The researchers suggest that by being able to track how delays or disturbances play out at each station under different conditions, they'll be able to predict where congestion is likely to occur in the system and plan around it.

But it's not just the major stations. They're also looking into the ways that closures or delays at even minor stations affect traffic and flows at other surrounding stations. The idea is that having this understanding of the system will better equip TfL to reduce the congestion and impact of even temporary closures and delays.

This video explains the process and shows some cool visualizations of what the crowds look like at each station.

via GIS and Agent-Based Modeling

Image courtesy YouTube user UCLEngineering

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