Dynamic Traffic Modeling from Overhead Imagery

Scott Workman, Nathan Jacobs

Abstract

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.

CVPR 2020 Oral Paper

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Citation

@inproceedings{workman2020dynamic,
  author={Scott Workman and Nathan Jacobs},
  title={{Dynamic Traffic Modeling from Overhead Imagery}},
  booktitle={{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
  year=2020
}

Oral Presentation

Dynamic Traffic Speeds Dataset

We introduce a new dataset for fine-grained road understanding that combines high resolution overhead imagery with a year of historical traffic speeds for New York City. Refer to the supplemental material for additional information about the dataset. Please contact us to receive access to the database.