Probabilistic Image-Driven Traffic Modeling via Remote Sensing

• URL: http://arxiv.org/abs/2403.05521v2
• Date: Thu, 18 Jul 2024 16:35:23 GMT
• Title: Probabilistic Image-Driven Traffic Modeling via Remote Sensing
• Authors: Scott Workman, Armin Hadzic,
• Abstract summary: We introduce a multi-modal, multi-task transformer-based segmentation architecture that can be used to create dense city-scale traffic models. We evaluate our method extensively using the Dynamic Traffic Speeds benchmark dataset and significantly improve the state-of-the-art.
• Score: 8.234589405189187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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