When better traffic forecasts fail to improve signal control: a layered diagnostic study of forecast-to-decision value
Quick Answer
This study reveals that enhanced traffic forecasts do not guarantee improved signal control decisions, as evidenced by a 6.09% increase in queue vehicle-seconds despite a 90% conformal interval achieving 90.72% marginal coverage.
Quick Take
The research highlights the importance of temporal observability and action identifiability in translating predictive improvements into operational benefits.
Key Points
- Entry-level forecasts reduced mean absolute error by 4.03% compared to historical data.
- Only two of nine intersections provided multiple effective control actions.
- Causal forecasts increased queue vehicle-seconds by 6.09% on frozen test dates.
- A synthetic positive control showed a 61.5% reduction in internal rollout costs.
- Forecast value is contingent on observability, action identification, and objective alignment.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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