Traffic Prediction
112 papers with code • 32 benchmarks • 18 datasets
Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.
( Image credit: BaiduTraffic )
Libraries
Use these libraries to find Traffic Prediction models and implementationsLatest papers
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting
Additionally, when handling traffic data, researchers tend to manually design the model structure based on the data features, which makes the structure of traffic prediction redundant and the model generalizability limited.
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years.
BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations.
RGDAN: A random graph diffusion attention network for traffic prediction
RGDAN comprises a graph diffusion attention module and a temporal attention module.
MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data
This model transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids.
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems.
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes
Moreover, Given the dynamic nature of traffic, the need for real-time traffic modeling also becomes crucial to ensure accurate and up-to-date traffic predictions.
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space
On the other hand, the model unconditionally learns the probability distribution of the data $p(X)$ and generates samples that conform to this distribution.
SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting
Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.
Spatio-Temporal-Decoupled Masked Pre-training: Benchmarked on Traffic Forecasting
Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.