13th Machine Learning Meetup

For our next meetup, we have the nice folks at UCL hosting us. We'll have three shorter talks by UCL stats & ML researchers. See you there!

Title: Nodes from the Underground: Modelling Traffic Patterns and Shock Effects in the London Transportation Network

Speaker: Ricardo Silva (http://www.homepages.ucl.ac.uk/~ucgtrbd/), Statistical Science, UCL

Transportation networks play a major role in the economy and social structure of large cities. In such complex systems, local unplanned disruptions happen due to a variety of unforeseen events such as power and mechanical failures. The effect of disruptions on passenger behaviour is of fundamental interest. For the passenger's point of view, this translates into delays, station closures or line closures. In particular, it is important to predict changes on how passengers leave the system at different locations within the affected regions. This is a first and essential step at assessing which components of the system are prone to overcrowding, and how they are jointly affected. For instance, dynamic changes to the bus system might potentially be implemented to compensate for increases of passenger evasion along several points in a route.
In this talk, we discuss how we can model disruptions in the London fast train network, namely the system comprised of the Underground, Overground and DLR. We measure effects by passengers' exit rates, modelling them via a combination of parametric and nonparametric models. Crucially, due to the relative sparseness of disruption events, we show how to leverage the data obtained under the "natural regime" of the system, where no disruptions take place, as as features that are predictive of the anomalous behaviour. We show the effectiveness of our findings on data collected from smart card users of the London Tube during 70 days spread across 2011 and 2012. The relationship is remarkably robust across different shocks, and easily expressible as a linear model. Our framework is however very general, and provides a recipe for similar analyses in any large urban transport network.