ASTER: Adaptive Spatio-Temporal Early Decision Model for Dynamic Resource Allocation

TL;DR

ASTER is a novel framework that integrates adaptive spatio-temporal modeling with decision-making to improve resource allocation in dynamic environments, especially in emergency response scenarios.

Contribution

It introduces a unified model that directly supports decision-making from spatio-temporal data, combining a new interaction module and a reinforcement learning-based decision agent.

Findings

Achieves state-of-the-art early prediction accuracy.

Enhances resource allocation efficiency in experiments.

Outperforms existing methods on multiple metrics.

Abstract

Supporting decision-making has long been a central vision in the field of spatio-temporal intelligence. While prior work has improved the timeliness and accuracy of spatio-temporal forecasting, converting these forecasts into actionable strategies remains a key challenge. A main limitation is the decoupling of the prediction and the downstream decision phases, which can significantly degrade the downstream efficiency. For example, in emergency response, the priority is successful resource allocation and intervention, not just incident prediction. To this end, it is essential to propose an Adaptive Spatio-Temporal Early Decision model (ASTER) that reforms the forecasting paradigm from event anticipation to actionable decision support. This framework ensures that information is directly used for decision-making, thereby maximizing overall effectiveness. Specifically, ASTER introduces a new Resource-aware Spatio-Temporal interaction module (RaST) that adaptively captures long- and short-term dependencies under dynamic resource conditions, producing context-aware spatiotemporal representations. To directly generate actionable decisions, we further design a Preference-oriented decision agent (Poda) based on multi-objective reinforcement learning, which transforms predictive signals into resource-efficient intervention strategies by deriving optimal actions under specific preferences and dynamic constraints. Experimental results on four benchmark datasets demonstrate the state-of-the-art performance of ASTER in improving both early prediction accuracy and resource allocation outcomes across six downstream metrics.