Traffic Forecasting and Scene Understanding

The field of traffic forecasting and scene understanding is moving towards more accurate and efficient models that can capture complex spatial-temporal dependencies. Recent developments have focused on leveraging self-attention mechanisms, machine learning approaches, and hybrid frameworks to improve predictive performance. Notably, the use of spatio-temporal information and vision-language models is becoming increasingly important for traffic scene understanding.

Some noteworthy papers in this area include: Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach, which proposes a novel Spatial-Temporal Self-Attention Model for traffic forecasting. Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting, which introduces a novel attention-based model that applies Kronecker product approximations to decompose spatiotemporal attention. Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding, which proposes a novel SpatioTemporal Enhanced Model based on CILP for traffic scene understanding. HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting, which proposes a novel framework that decouples traffic data into periodic and residual components.

Sources

Capturing Complex Spatial-Temporal Dependencies in Traffic Forecasting: A Self-Attention Approach

Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach

Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting

Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding

HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting

How does the Performance of the Data-driven Traffic Flow Forecasting Models deteriorate with Increasing Forecasting Horizon? An Extensive Approach Considering Statistical, Machine Learning and Deep Learning Models

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