稀疏固定传感器下高速公路全路网速度估计与预测的模型选择研究

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Abstract:Most freeway agencies have fixed detectors on only part of their network but need speeds for all of it. This paper compares methods for estimating current and next-hour speeds on every link from such sparse detectors, using historical probe data for training. The test network is the Dallas--Fort Worth freeway system: 4,127 directional links, of which 643 (15.6\%) carry a side-fire detector. Eighteen methods from five families (non-learned baselines, statistical estimators, tabular learners, road-graph neural networks and models that learn spatial relations) were trained under one protocol with three random seeds. Each neural architecture was also retrained with a parameter-free, direction-aware kernel interpolation of the sensor readings as additional input. Graph neural networks that pass information along the road graph, including a plain graph convolutional recurrent network, reduced the current-hour error on unsensored links by about 17\% relative to the historical average. Models with adaptive graphs, attention or identity embeddings stayed close to the historical average and did not fit the training data well, indicating that they could not learn which detectors matter for which links. With the interpolation features, all ten architectures came within about 0.1 mph of each other. The remaining error depended mainly on coverage: almost all of the improvement was found within about 3 mi of a detector, and very little on tollways without detectors or on links with no road-graph connection to one. The results are summarised as guidance on model choice and detector placement.

Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.22630 [cs.LG]
	(or arXiv:2610.22630v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.22630 arXiv-issued DOI via DataCite

Submission history

From: Ran Tu [view email]
[v1] Thu, 15 Oct 2026 20:35:45 UTC (819 KB)
[v2] Mon, 26 Oct 2026 13:40:34 UTC (866 KB)