用多智能体闭环框架，从异构数据中精准估算乘客载量。
A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams
- 基于状态中心的多智能体架构，每步都保证物理合理性。
- 动态分配信任度，有效处理传感器误差与证据冲突。
- 适合城市交通运营方和智能调度系统开发者使用。
为支持运营与面向乘客的服务，交通机构需要可靠的乘客载量轨迹。当前载量估计通常依赖不完善的感知系统，而非完全观测数据，且现代自动乘客计数（APC）系统的准确性仍受站台布局、客流强度和运行条件影响。针对异构数据流中增量计数误差、证据冲突及传感器可靠性依赖上下文等挑战，本文提出一种闭环、状态中心、多智能体框架。该方法在每一步强制物理可行性，动态分配各证据源的信任度，并将物理约束违反残差反馈至训练以提升鲁棒性。整体架构包括统一的站点事件主干、耦合的感知-物理-融合循环（逐站推断），以及可选的行程级宏观校正与闭环校准模块。
原文摘要 · Abstract (English)
To support operations and passenger-facing services, transit agencies need reliable passenger load trajectories. Currently, load estimates are typically inferred from imperfect sensing systems rather than fully observed, and the accuracy of modern automatic passenger counting (APC) systems still varies with station layout, flow intensity, and operating conditions. To address the challenges of robust passenger load estimation from heterogeneous data streams, including incremental count errors, evidence conflicts, and context-dependent sensor reliability, we propose a closed-loop, state-centric, multi-agent framework. This method enforces physical feasibility at every step, allocates trust dynamically among evidence sources, and feeds physics-derived violation residuals back into training for robustness improvement. The architecture consists of a unified stop-event backbone, a coupled Perception--Physical--Fusion loop for stop-by-stop inference, and optional trip-level macro-correction and closed-loop calibration modules.
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