arXiv：cs.AI· Jianing Long, Xiaobin Li, Wuming Lei, Weiguang Wang·· 7 小时前AI 评分38
更好的交通预测为何未能改善信号控制：一项预测到决策价值的分层诊断研究
When better traffic forecasts fail to improve signal control: a layered diagnostic study of forecast-to-decision value
AI 导读
一项基于宣城 29 天重建需求数据的分层诊断研究显示，交通预测精度提升未必带来更好的信号控制决策：入口级和车流级预测相对历史均值分别降低 MAE 4.03% 和 3.92%，名义 90% conformal 区间边际覆盖率达 90.72%，但在事后高需求子集上仅 75.66%。
正文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.06992 [cs.AI] |
| (or arXiv:2610.06992v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06992 arXiv-issued DOI via DataCite |
Submission history
From: Xiaobin Li [view email]
[v1]
Sun, 4 Oct 2026 07:39:32 UTC (430 KB)
来源：arXiv：cs.AI · arxiv.org