Human centered artificial intelligence for smart city traffic coordination

Abstract

Smart city traffic systems increasingly rely on edge intelligence and distributed learning to coordinate vehicles and infrastructure. However, many existing approaches prioritize system-level optimization while overlooking how humans perceive, interpret, and respond to automated decisions, limiting efficiency and real-world adoption. This paper proposes a human-centric edge intelligence framework that integrates federated reinforcement learning (FRL) with interpretable human–machine interfaces for urban traffic coordination. Vehicles and signalized intersections operate as decentralized learning agents, collaboratively optimizing traffic flow while retaining data locally. Human-centered constraints are embedded directly into the learning process through reward design and action-space restrictions, ensuring that system decisions can be expressed as clear, actionable guidance via augmented reality head-up displays and voice prompts. We present a layered edge architecture and a smart-intersection scenario to demonstrate how sensing, learning, communication, and human interaction are jointly coordinated. A simulation-based evaluation pipeline using SUMO and CARLA is introduced to examine corridor-level traffic dynamics, visual perception scenarios, and human-facing advisory interactions. SUMO is used for microscopic traffic-flow and signal-control modeling, while CARLA supports visual validation of safety-critical events and interface-level driver advisories. A dedicated SUMO–CARLA workflow diagram is provided to clarify how traffic-state generation, FRL decision-making, visual scenario reconstruction, and HMI advisory evaluation are connected. To ground the design in real-world evidence, we additionally report a descriptive trajectory analysis of the public NGSIM Peachtree Street dataset; this analysis characterizes speed, acceleration, and following-headway behavior and motivates the proposed reward and advisory-trigger design, but it is a descriptive/motivational analysis of existing trajectory data rather than an evaluation of the proposed FRL framework. This paper is explicitly positioned as a conceptual and methodological framework contribution: no FRL policy has been trained, no SUMO–CARLA simulation has been executed, and no quantitative comparison or ablation results are reported. These experiments constitute the planned next phase of this research program. Because the framework is part of an ongoing security-sensitive smart-city research effort, deployment-specific control interfaces and protected configuration details are reported at an abstracted level, while the non-sensitive simulation structure, evaluation metrics, and agent-level learning process are described for reproducibility. The proposed framework provides a practical pathway for evaluating intelligent mobility systems that balance decentralized optimization, privacy preservation, interpretability, scalability, and human-aligned operation.