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. We benchmark the controller in a SUMO traffic simulator against a rule-based heuristic and a deep Q-network (DQN) across four scenarios that progressively increase noise and nonstationarity, spanning sensor occlusion, adverse weather, and stochastic accidents. Across 100 independent random evaluations per scenario, active inference attains the lowest idle times and CO2 emissions in the noisiest scenarios (56,977 s and 29.12 kg vs. 71,741 s and 30.56 kg for DQN). These gains come at a modest cost in bus priority service rate and phase switch frequency.

翻译：城市交通信号控制在配备物联网传感器的交叉路口必须保持有效性，以应对传感器遮挡、天气衰减和非平稳需求等挑战。传统控制器在此类条件下性能下降，而基于学习的策略仍难以审计。为了解决这些问题，我们提出了一种面向四臂信号化交叉口的主动推断控制器，通过最小化基于各方向拥堵水平高斯信念的期望自由能（EFE）动态选择相位，从而形成完全可追溯的决策流程。我们在SUMO交通仿真器中，将该控制器与基于规则的启发式方法及深度Q网络（DQN）进行对比，测试了四种场景，这些场景逐步增加噪声与非平稳性，涵盖传感器遮挡、恶劣天气及随机事故。通过每个场景下100次独立随机评估，主动推断在最嘈杂场景中取得了最低的空闲时间和二氧化碳排放量（分别为56,977秒和29.12千克，而DQN为71,741秒和30.56千克）。这些性能提升以公交优先服务率和相位切换频率的适度牺牲为代价。