用直方图距离量化交通信号控制中的分布偏移，预测性能下降。
Quantifying Distribution Shift in Traffic Signal Control with Histogram-Based GEH Distance
- 将交通场景建模为需求直方图，用改进的GEH距离度量分布差异。
- 距离越大，平均行程时间越长，通行量越低，尤其对学习型控制效果显著。
- 方法不依赖具体策略，可解释性强，适合用于算法评估与训练设计。
交通信号控制算法易受分布偏移影响，在与设计或训练时不同的交通条件下性能下降。本文提出一种系统方法，将交通场景表示为需求直方图，并通过基于GEH的距离函数量化分布偏移。该方法不依赖具体控制策略，具有可解释性，且利用了交通工程中广泛使用的统计指标。我们在20个仿真场景中验证了该方法，对比了NEMA感应式控制器和强化学习控制器（FRAP++）。结果表明，场景距离越大，平均行程时间越长，通行量越低，对学习型控制的解释力尤为强。整体上，该方法在预测分布偏移下的性能退化方面优于已有技术。研究结果凸显了该框架在基准测试、训练方案设计和运行监控中的应用价值。
原文摘要 · Abstract (English)
Traffic signal control algorithms are vulnerable to distribution shift, where performance degrades under traffic conditions that differ from those seen during design or training. This paper introduces a principled approach to quantify distribution shift by representing traffic scenarios as demand histograms and comparing them with a GEH-based distance function. The method is policy-independent, interpretable, and leverages a widely used traffic engineering statistic. We validate the approach on 20 simulated scenarios using both a NEMA actuated controller and a reinforcement learning controller (FRAP++). Results show that larger scenario distances consistently correspond to increased travel time and reduced throughput, with particularly strong explanatory power for learning-based control. Overall, this method can predict performance degradation under distribution shift better than previously published techniques. These findings highlight the utility of the proposed framework for benchmarking, training regime design, and monitoring in adaptive traffic signal control.
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