Probabilistic Image-Driven Traffic Modeling via Remote Sensing
<|reference_start|>Probabilistic Image-Driven Traffic Modeling via Remote Sensing: 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.<|reference_end|>
@article{workman2024probabilistic,
    title={Probabilistic Image-Driven Traffic Modeling via Remote Sensing},
    author={Scott Workman, Armin Hadzic},
    journal={arXiv preprint arXiv:2403.05521},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05521},
    primaryClass={cs.CV}
}