arXiv:2605.25004v1 Announce Type: new Abstract: Inferring network-wide traffic states from sparse observations with high accuracy and trustworthy uncertainty quantification is essential for intelligent transportation systems, yet it remains challenging due to the underdetermined nature of the problem, multifaceted disturbances in sensing networks, and the inherent conflicts among multiple inference sub-tasks when modeled jointly. We propose the Task-Aware Attentive Neural Process (TA-ANP), a unified probabilistic framework for resilient and trustworthy global traffic state inference (GTSI) by
The increasing complexity and scale of urban environments necessitate more robust and scalable AI solutions for critical infrastructure like traffic management, aligning with broader advancements in AI.
Sophisticated and trustworthy AI for traffic management can significantly improve urban efficiency, reduce congestion, and enhance safety, impacting economic productivity and quality of life.
The development of resilient and trustworthy AI frameworks like TA-ANP marks a step towards more reliable and adaptable intelligent transportation systems, moving beyond basic predictive models.
- · Smart city developers
- · Urban planners
- · Logistics companies
- · Commuters
- · Legacy traffic management systems
- · Cities without AI integration
Improved traffic flow reduces commute times and fuel consumption.
More efficient urban mobility could spur economic growth in metropolitan areas by facilitating commerce and services.
The success of trustworthy AI in traffic could accelerate its adoption in other critical public infrastructure management, leading to broader automation.
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Read at arXiv cs.LG