mlsanityData to 7 Oct 2026

An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation

arXiv 2302.10400 ↗First posted 21 Feb 2023cs.LGcited by 1

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Abstract (arXiv)

Real-time traffic state estimation is essential for intelligent transportation systems. The NeurIPS 2022 Traffic4cast challenge provides an excellent testbed for benchmarking short-term traffic state estimation approaches. This technical report describes our solution to this challenge. In particular, we present an efficient two-stage gradient boosting framework for short-term traffic state estimation. The first stage derives the month, day of the week, and time slot index based on the sparse loop counter data, and the second stage predicts the future traffic states based on the sparse loop counter data and the derived month, day of the week, and time slot index. Experimental results demonstrate that our two-stage gradient boosting framework achieves strong empirical performance, achieving third place in both the core and the extended challenges while remaining highly efficient. The source code for this technical report is available at urlhttps://github.com/YichaoLu/Traffic4cast2022.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 58 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

19 of 58 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  7. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  8. 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 180
  9. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 179
  10. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  11. 2019Graph Neural Networks for Modelling Traffic Participant Interactioncited by 42
  12. 2022Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge networkcited by 34
7 more
  1. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  2. 2019ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modelingcited by 19
  3. 2019CrevNet: Conditionally Reversible Video Predictioncited by 15
  4. 2022Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processescited by 15
  5. 2021Learning to Transfer for Traffic Forecasting via Multi-task Learningcited by 7
  6. 2022Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networkscited by 3
  7. 2019Learning Effective Visual Relationship Detector on 1 GPUcited by 2

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectorscited by 3