title: Predicting traffic volumes and estimating the effects of shocks in massive transportation systems.
abstract: Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.
authors.affiliation: Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, London WC1E 6BT, United Kingdom; ricardo@stats.ucl.ac.uk.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Silva
authors.affiliation: Department of Management Science and Innovation, University College London, London WC1E 6BT, United Kingdom; and.
authors.forename: Soong Moon
authors.initials: SM
authors.lastname: Kang
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138.
authors.forename: Edoardo M
authors.initials: EM
authors.lastname: Airoldi
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data* / Q000639:trends; D000465:Algorithms; D002947:Cities; D002958:City Planning / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends; D004779:Environment Design / Q000706:statistics & numerical data / Q000639:trends; D005544:Forecasting; D006801:Humans; D008131:London; D008377:Maps as Topic; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D014186:Transportation / Q000379:methods / Q000706:statistics & numerical data*
keywords: complex systems; regime change; smart cities; transportation