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With people constantly migrating to different urban areas, our mobility needsfor work, services and leisure are transforming rapidly. The changing urbandemographics pose several challenges for the efficient management of transitservices. To forecast transit demand, planners often resort to sociologicalinvestigations or modelling that are either difficult to obtain, inaccurate oroutdated. How can we then estimate the variegated demand for mobility? Wepropose a simple method to identify the spatiotemporal demand for publictransit in a city. Using a Gaussian mixture model, we decompose empiricalridership data into a set of temporal demand profiles representative ofridership over any given day. A case of approximately 4.6 million daily transittraces from the Greater London region reveals distinct demand profiles. We findthat a weighted mixture of these profiles can generate any station trafficremarkably well, uncovering spatially concentric clusters of mobility needs.Our method of analysing the spatiotemporal geography of a city can be extendedto other urban regions with different modes of public transit.

Extracting Spatiotemporal Demand for Public Transit from Mobility Data