mlsanityData to 7 Oct 2026

Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline

arXiv 2403.14941 ↗First posted 22 Mar 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable direction. However, further progress in this field is hindered by the absence of comprehensive and unified evaluation standards, coupled with limited public availability of data and code. In this paper, we present the first systematic classification framework for lane-level traffic prediction, offering a structured taxonomy and analysis of existing methods. We construct three representative datasets from two real-world road networks, covering both regular and irregular lane configurations, and make them publicly available to support future research. We further establishes a unified spatial topology structure and prediction task formulation, and proposes a simple yet effective baseline model, GraphMLP, based on graph structure and MLP networks. This unified framework enables consistent evaluation across datasets and modeling paradigms. We also reproduce previously unavailable code from existing studies and conduct extensive experiments to assess a range of models in terms of accuracy, efficiency, and applicability, providing the first benchmark that jointly considers predictive performance and training cost for lane-level traffic scenarios. All datasets and code are released at https://github.com/ShuhaoLii/LaneLevel-Traffic-Benchmark.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 22 Mar 2024.

Builds on

21 of 56 references.

References · 21

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9 more
  1. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  2. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  3. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  4. 2022Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfercited by 43
  5. 2020RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Frameworkcited by 33
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  7. 2023Automated Spatio-Temporal Graph Contrastive Learningcited by 24
  8. 2023ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecastingcited by 21
  9. 2019Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impactcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generationcited by 2
  2. 2026Uncertainty-Aware Trajectory Prediction: A Unified Framework Harnessing Positional and Semantic Uncertaintiescited by 0
  3. 2026Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twinscited by 0