Probabilistic Image-Driven Traffic Modeling via Remote Sensing
This work addresses traffic modeling for urban planning and mobility applications, but it is incremental as it extends existing image-driven approaches.
The paper tackles modeling spatiotemporal traffic patterns from overhead imagery by introducing a multi-modal, multi-task transformer-based segmentation architecture, achieving significant improvements in state-of-the-art on the DTS benchmark dataset.
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.