The human world is full of things that move: people commuting, goods being shipped, trains and cars and taxis etc. Sensors for recording these movements are becoming cheaper and more widespread, from GPS-equipped mobile phones to smart travelcards, and so there is an explosion in the amount of movement data that is available. How can this wealth of data be understood, and how can it be put to work to improve the way the world moves?

I will present a system for working with big data about urban mobility, which my colleagues and I at Urban Engines have spent two years building. It has three parts: algorithms for stitching together partial or noisy observations to reconstruct a fine-grained "digital replica"; interactive visualizations and a query language for answering questions and building models about moving things; and integration with simulation and other operations-research tools to answer "what if?” questions.

We have tried to build a data science tool for users who are comfortable with Excel but not with probability or operations research. Based on our experiences, I will discuss the promise of big data and how it might make network modeling accessible to a broader audience.