Complex transport systems used by millions of people per day, such as the London Underground, must respond quickly and effectively to unpredictable, unplanned disruptions that cause stations or parts of a line to close down. This project concerns the development of models for passenger movement and reaction to closures, in order to learn how demand and usage fluctuate over different spatial regions of a transport system and different times of the day.

Explaining the science

The methodological basis for this project is probabilistic machine learning, where we model the behaviour of a fast rail transport system in terms of a network tomography problem, akin to those encountered in medical imaging: we observe part of the passengers’ behaviour and infer unobserved quantities of interest.

In particular, latent passenger trajectories and train availability are quantities which can be inferred indirectly and partially validated with data graciously provided by Transport for London. These are hard computational problems, and the methodology will rely in particular on stochastic (i.e. random) variational inference.

Prediction of passengers' behaviour during a transport disruption at Victoria underground station.
Prediction of passengers' behaviour during a transport disruption at Victoria underground station.


Project aims

The most crucial aspect of the research will advance the state of the art on how to leverage historical data from past disruptions to understand what happens under unseen situations. This will combine background knowledge on causal effects of disruptions on passengers extracted from the cross-section of the system, along with modern predictive modelling.

The project will allow for a better understanding of demand that has not been met when passengers decide to stop their journey earlier, and how delays can be propagated by crowds changing their route. This has direct implications on the safety of passengers by providing tools to improve crowd management within a system like the Underground, and by better compensating for related, predicted excess demand on other public transport such as buses.


Researchers and collaborators

Contact info

[email protected]