Extracting Spatiotemporal Demand for Public Transit from Mobility Data
arXiv:2006.03351
Abstract
With people constantly migrating to different urban areas, our mobility needs for work, services and leisure are transforming rapidly. The changing urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations or modelling that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variegated demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of approximately 4.6 million daily transit traces from the Greater London region reveals distinct demand profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our method of analysing the spatiotemporal geography of a city can be extended to other urban regions with different modes of public transit.
arXiv comment12 pages, 5 figures, submitted for peer review, SI included
References in corpus (6)
- Random Walks on Multiplex Networks
- Structure of urban movements: polycentric activity and entangled hierarchical flows
- From mobile phone data to the spatial structure of cities
- Uncovering the spatial structure of mobility networks
- Comparing and modeling land use organization in cities
- Emergence of hierarchy in cost driven growth of spatial networks