mlsanityData to 7 Oct 2026

Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs

arXiv 2303.05660 ↗First posted 10 Mar 2023stat.ML cs.LGcited by 9

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Abstract (arXiv)

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 79 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

21 of 79 references.

References · 21

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9 more
  1. 2021Spatial Aggregation and Temporal Convolution Networks for Real-time Krigingcited by 27
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.

9 papers.

  1. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  2. 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
  3. 2024Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approachcited by 6
  4. 2025Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approachcited by 4
  5. 2023Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scalecited by 3
  6. 2025MoGERNN: An Inductive Traffic Predictor for Unobserved Locationscited by 2
  7. 2025Calibration of Vehicular Traffic Simulation Models by Local Optimizationcited by 2
  8. 2025Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attentioncited by 2
  9. 2026Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Datacited by 0