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 implementations

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Kqingzheng/ModWaveMLP The 38th Annual AAAI Conference on Artificial Intelligence 2024

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.

3
01 Jul 2024

Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline

shuhaolii/tits24lanelevel-traffic-benchmark 22 Mar 2024

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.

2
22 Mar 2024

BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

ChyaZhang/ChatTraffic 8 Mar 2024

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.

23
08 Mar 2024

RGDAN: A random graph diffusion attention network for traffic prediction

wengwenchao123/RGDAN journal 2024

RGDAN comprises a graph diffusion attention module and a temporal attention module.

7
16 Jan 2024

MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data

zachysun/taxi_traffic_benchmark 16 Jan 2024

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.

7
16 Jan 2024

Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting

pengxin-guo/adcsd 8 Jan 2024

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.

3
08 Jan 2024

Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes

himanshudce/owam 27 Dec 2023

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.

1
27 Dec 2023

SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space

li-yun-chen/spd-ddpm 13 Dec 2023

On the other hand, the model unconditionally learns the probability distribution of the data $p(X)$ and generates samples that conform to this distribution.

0
13 Dec 2023

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

Pachark/SVQ-Forecasting 6 Dec 2023

Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.

2
06 Dec 2023

Spatio-Temporal-Decoupled Masked Pre-training: Benchmarked on Traffic Forecasting

jimmy-7664/std_mae 1 Dec 2023

Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.

24
01 Dec 2023