发表机构
East China Jiaotong University（华东交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过层次化诊断框架，利用宣城交通数据揭示预测精度提升未必改善信号控制，指出价值受限于时间可观测性、动作可识别性、动态一致性与目标对齐，并提供实用诊断协议。
AI 中文摘要
改进的交通预测并不必然带来更好的信号控制决策。我们通过一项层次化诊断研究来探究这一差距，该研究使用了来自中国宣城的29天重建需求数据，其中7天留作测试。该框架评估了点预测、共形区间、依赖感知场景以及匹配的闭环控制器。入口级和运动级预测相对于历史均值分别将平均绝对误差降低了4.03%和3.92%。名义上90%的共形区间实现了90.72%的边际覆盖率，但在事后高需求子集上仅为75.66%。接口审计识别出决策时间泄漏，并揭示九个受控交叉口中仅有两个提供多种有效动作。我们修正了时间接口，并使用穷举联合动作搜索将因果预测与五秒事件预言机进行比较。一个合成阳性对照表明，未来信息可以将内部滚动成本降低61.5%。然而，在冻结的测试日期上，因果预测和事件预言机相对于匹配的无未来滚动分别增加了6.09%和3.39%的队列车辆秒数，而预言机将溢出暴露减少了3.78%；配对日期的自助法区间跨越零。这些发现表明，预测价值取决于时间可观测性、动作可识别性、动态一致性和目标对齐。所提出的协议提供了一种实用的方法，用于诊断预测改进未能转化为运营效益的情况。
英文摘要
Improved traffic forecasts do not necessarily yield better signal-control decisions. We investigate this gap through a layered diagnostic study using 29 days of reconstructed demand from Xuancheng, China, with seven dates reserved for testing. The framework evaluates point forecasts, conformal intervals, dependence-aware scenarios, and matched closed-loop controllers. Entry-level and movement-level forecasts reduce mean absolute error by 4.03% and 3.92%, respectively, relative to historical means. A nominal 90% conformal interval achieves 90.72% marginal coverage but only 75.66% on an ex-post high-demand subset. Interface audits identify decision-time leakage and reveal that only two of nine controlled intersections offer multiple effective actions. We correct the temporal interface and compare causal forecasts with a five-second event oracle using exhaustive joint-action search. A synthetic positive control demonstrates that future information can reduce the internal rollout cost by 61.5%. On the frozen test dates, however, causal forecasts and the event oracle increase queue vehicle?seconds by 6.09% and 3.39% relative to the matched no-future rollout, while the oracle reduces spillback exposure by 3.78%; paired-day bootstrap intervals cross zero. These findings indicate that forecast value depends on temporal observability, action identifiability, dynamics consistency, and objective alignment. The proposed protocol provides a practical way to diagnose where predictive improvements fail to translate into operational benefits.
