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December 5, 2025Software Practice and Experience

Adaptive and Dynamic Spatio‐Temporal Network for Traffic Flow Forecasting

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Authors

YXYing XingBYBin YangTLTianyu Lu

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Overview

Proposed model improves dynamics of urban transportation systems in traffic congestion, indicating efficient forecasting methods.

Key Points

  • Traffic congestion poses significant challenges in urban transportation systems, affecting overall city functioning.
  • The adaptive dynamic model enhances local dynamics and improves forecasting accuracy for traffic flow disruptions.
  • Using an innovative approach, the model addresses key spatial dependencies and temporal relationships effectively.
  • The findings support emerging strategies for managing urban traffic congestion, highlighting potential for broader applications.

Cite This Study

Xing et al. (2025) studied this question.

synapsesocial.com/papers/694023c82d562116f28fcb0chttps://doi.org/10.1002/spe.70034
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Also Consider

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  1. 1Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction2024 · 6 citations
  2. 2A novel adaptive spatio-temporal dual-branch model for traffic flow prediction2025
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  4. 4Dynamic spatial‐temporal network for traffic forecasting based on joint latent space representation2024
  5. 5STIL-TA: A new model of traffic flow forecasting based on spatiotemporal interactive learning and temporal attention2025