Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
arXiv:2606.13698v1 Announce Type: cross Abstract: Urban traffic signal control at IoT-instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand. Conventional controllers degrade under these conditions, and learned policies remain difficult to audit. To address these challenges, we propose an active inference controller for a four-arm signalized intersection that dynamically selects phases by minimizing expected free energy (EFE) over Gaussian beliefs about per-direction congestion levels, yielding a fully traceable decision pipeline.
The proliferation of IoT devices in urban infrastructure and the increasing complexity of traffic patterns necessitate more robust and adaptive control systems, pushing research towards advanced AI techniques.
This development represents a significant step towards more resilient and auditable AI systems managing critical urban infrastructure, directly addressing limitations of current conventional and learned controllers.
Traffic management systems can become more adaptive and reliable under adverse conditions, with decision-making processes that are traceable and explainable, improving urban mobility and safety.
- · Smart city solution providers
- · Urban planning departments
- · AI-driven infrastructure companies
- · Citizens
- · Legacy traffic control manufacturers
- · Cities with static infrastructure
- · Conventional traffic modeling firms
More efficient urban traffic flow reduces congestion, fuel consumption, and pollution.
Improved traffic management frees up urban space and resources for other development, potentially influencing real estate and public transport strategies.
The success of traceable AI in traffic control could accelerate its adoption in other critical public infrastructure, increasing demand for auditable and explainable AI systems.
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Read at arXiv cs.AI