mlsanityData to 7 Oct 2026

Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey

arXiv 2102.09759 ↗First posted 19 Feb 2021cs.LG stat.MLcited by 14

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Abstract (arXiv)

Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of service of the transportation network. With increasing access to larger datasets of higher resolution, the relevance of deep learning for such tasks is increasing. Several comprehensive survey papers in recent years have summarised the deep learning applications in the transportation domain. However, the system dynamics of the transportation network vary greatly between the non-congested state and the congested state -- thereby necessitating the need for a clear understanding of the challenges specific to congestion prediction. In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion. Recurring and non-recurring congestion are discussed separately. Our survey leads us to uncover inherent challenges and gaps in the current state of research. Finally, we present some suggestions for future research directions as answers to the identified challenges.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 31 of its 156 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

31 of 156 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 4; 2025: 1; 2026: 1.

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  1. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  2. 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
  3. 2022Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Reviewcited by 7
  4. 2024Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Predictioncited by 7
  5. 2022Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysiscited by 6
  6. 2021Traffic Forecasting on Traffic Moving Snippetscited by 3
  7. 2024Beyond Words: Evaluating Large Language Models in Transportation Planningcited by 3
  8. 2022Multimodal and Crossmodal AI for Smart Data Analysiscited by 1
  9. 2023Uncertainty Quantification for Image-based Traffic Prediction across Citiescited by 1
  10. 2024Context-aware knowledge graph framework for traffic speed forecasting using graph neural networkcited by 1
  11. 2023Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graphcited by 0
  12. 2023Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportationcited by 0
2 more
  1. 2023A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Predictioncited by 0
  2. 2026Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decompositioncited by 0