When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework
arXiv:2606.26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes. Electric bus fleets provide a relevant test case. Their operation requires continuous coordination between service reliability, battery state-of-charge, charger availability, electricity prices, route-energy uncertainty, and vehicle-to-grid (V2G) opportunities. This paper proposes an agentic aggregator framework that streamlines this decision environment by coupling an optimizat
The increasing complexity of electric fleet operations, coupled with advancements in AI agentic systems, makes an aggregator framework for optimized coordination timely.
This development highlights the practical application of AI agents in critical infrastructure, demonstrating their potential to optimize complex logistical and energy management challenges.
Operational efficiency and strategic decision-making in electric bus fleet management can be significantly enhanced through autonomous AI systems, potentially setting a precedent for other sectors.
- · AI software developers
- · Electric bus fleet operators
- · Smart grid developers
- · Urban planning agencies
- · Inefficient fleet management systems
- · Traditional energy management consultants
Optimized electric bus operations lead to reduced costs, improved service reliability, and more efficient grid integration.
The success of this agentic framework could accelerate the adoption of similar AI-driven orchestration in other complex public and private sector logistics.
Widespread integration of such systems could create new regulatory challenges and ethical considerations regarding autonomous decision-making in public services.
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Read at arXiv cs.AI