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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Silva, Ricardo Kang, Soong Moon Airoldi, Edoardo M. |
| Spatial Coverage | London |
| Description | Author Affiliation: Silva R ( Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, London WC1E 6BT, United Kingdom); Kang SM ( Department of Management Science and Innovation, University College London, London WC1E 6BT, United Kingdom); Airoldi EM ( Department of Statistics, Harvard University, Cambridge, MA 02138.); |
| 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. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 18 |
| Volume Number | 112 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2015-05-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Accidents, Traffic Statistics & Numerical Data Motor Vehicles Transportation Prevention & Control Algorithms City Planning Environment Design Maps As Topic Models, Theoretical Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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