Dynamic traffic modeling from overhead imagery

Scott Workman, Nathan Jacobs

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before a model can be used and it fails to generalize to new areas. Instead, we propose an automatic approach for generating dynamic maps of traffic speeds using convolutional neural networks. Our method operates on overhead imagery, is conditioned on location and time, and outputs a local motion model that captures likely directions of travel and corresponding travel speeds. To train our model, we take advantage of historical traffic data collected from New York City. Experimental results demonstrate that our method can be applied to generate accurate city-scale traffic models.

Original languageEnglish
Article number9157245
Pages (from-to)12312-12321
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

Bibliographical note

Funding Information:
Acknowledgements We gratefully acknowledge the financial support of NSF CAREER grant IIS-1553116.

Publisher Copyright:
© 2020 IEEE

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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