Probabilistic Image-Driven Traffic Modeling via Remote Sensing
Abstract
This work addresses the task of modeling spatiotemporal traffic patterns directly from overhead imagery, which we refer to as image-driven traffic modeling. We extend this line of work and introduce a multi-modal, multi-task transformer-based segmentation architecture that can be used to create dense city-scale traffic models. Our approach includes a geo-temporal positional encoding module for integrating geo-temporal context and a probabilistic objective function for estimating traffic speeds that naturally models temporal variations. We evaluate our method extensively using the Dynamic Traffic Speeds (DTS) benchmark dataset and significantly improve the state-of-the-art. Finally, we introduce the DTS++ dataset to support mobility-related location adaptation experiments.
Cite
Text
Workman and Hadzic. "Probabilistic Image-Driven Traffic Modeling via Remote Sensing." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73027-6_25Markdown
[Workman and Hadzic. "Probabilistic Image-Driven Traffic Modeling via Remote Sensing." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/workman2024eccv-probabilistic/) doi:10.1007/978-3-031-73027-6_25BibTeX
@inproceedings{workman2024eccv-probabilistic,
title = {{Probabilistic Image-Driven Traffic Modeling via Remote Sensing}},
author = {Workman, Scott and Hadzic, Armin},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73027-6_25},
url = {https://mlanthology.org/eccv/2024/workman2024eccv-probabilistic/}
}