  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.
