Abstract

Urban public transport operations are characterized by high variability, tight resource constraints, and the need for transparent and accountable decisions. Dispatchers and planners must react to disruptions, demand surges, and infrastructure constraints while meeting service-quality targets and maintaining sufficient fleet readiness. This paper presents a data-driven, multi-agent decision support model that treats operational control as a coordination problem among heterogeneous actors rather than a purely centralized optimization task. The approach integrates operational data streams typical for intelligent transportation systems with an agent-based environment in which organizational roles and managed objects are explicitly represented. The model supports “what-if” evaluation of feasible interventions under realistic constraints and links passenger-facing service KPIs with fleet readiness and decision-cycle indicators. The goal is to strengthen robustness of operational decisions and make trade-offs interpretable through explicit role logic and coordination mechanisms.

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