mlsanityData to 14 Oct 2026

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

arXiv 2305.19591 ↗First posted 31 May 2023cs.LG cs.AIcited by 16

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Abstract (arXiv)

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 65 of its 324 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

65 of 324 references.

References · 65

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38 more
  1. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  2. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  3. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
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  19. 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networkscited by 35
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 4; 2026: 4.

16 papers.

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  1. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  2. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  3. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  4. 2024Are Synthetic Time-series Data Really not as Good as Real Data?cited by 7
  5. 2024Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Predictioncited by 5
  6. 2023A Transfer Learning Framework for Proactive Ramp Metering Performance Assessmentcited by 3
  7. 2025MoGERNN: An Inductive Traffic Predictor for Unobserved Locationscited by 2
  8. 2024Context-aware knowledge graph framework for traffic speed forecasting using graph neural networkcited by 1
  9. 2024Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Predictioncited by 1
  10. 2023B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Predictioncited by 0
  11. 2025MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Predictioncited by 0
  12. 2025Deformable Dynamic Convolution for Accurate yet Efficient Spatio-Temporal Traffic Predictioncited by 0
4 more
  1. 2026Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twinscited by 0
  2. 2026A Collaborative Safety Shield for Safe and Efficient CAV Lane Changes in Congested On-Ramp Mergingcited by 0
  3. 2026Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimationcited by 0
  4. 2026Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periodscited by 0