RESEARCH ARTICLE

Urban traffic modeling and pattern detection using online map vendors and self-organizing maps

  • Zifeng Guo , 1 ,
  • Biao Li 2 ,
  • Ludger Hovestadt 1
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  • 1. Department of Architecture, Swiss Federal Institute of Technology Zurich (ETHZ), Zurich, 8093, Switzerland
  • 2. School of Architecture, Southeast University, Nanjing, 210096, China

Received date: 29 Jan 2021

Accepted date: 23 May 2021

Published date: 15 Dec 2021

Copyright

2021 2021 Higher Education Press Limited Company. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract

Typical traffic modeling approaches, such as network-based methods and simulation models, have been shown inadequate for urban-scale studies due to the fidelity issue of models. As a go-around, data-driven models have received increasing attention recently. However, most data-driven methods have been restricted by their data source and cannot be scaled up to manage urban- and regional-scale studies. Regarding this issue, this research proposes a pipeline that collects traffic data from online map vendors to bypass data limitations for large-scale studies. The study consists of two experiments: 1) recognizing the dominant traffic patterns of cities and 2) site-specific predictions of typical traffic or the most probable locations of patterns of interests. The experiments were conducted on 32 Swiss cities using traffic data that were collected for a two-month period. The results show that dominant patterns can be extracted from the temporal traffic data, and similar patterns exist not only in various parts of a city but also in different cities. Moreover, the results reveal that a country-level lockdown decreased traffic congestions in regional highways but increased those connections near the city centers and the country borders.

Cite this article

Zifeng Guo , Biao Li , Ludger Hovestadt . Urban traffic modeling and pattern detection using online map vendors and self-organizing maps[J]. Frontiers of Architectural Research, 2021 , 10(4) : 715 -728 . DOI: 10.1016/j.foar.2021.06.002

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