Traffic Prediction
114 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 )
Benchmarks
These leaderboards are used to track progress in Traffic Prediction
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Libraries
Use these libraries to find Traffic Prediction models and implementations
PeMSD8
METR-LA
PeMSD7
PeMSD4
Q-Traffic
Latest papers
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.
ChatTraffic: Text-to-Traffic Generation via Diffusion Model
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.
Spatio-Temporal Graph Mixformer for Traffic Forecasting
Mouradost/STGM
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Expert Systems with Applications 2023
Additionally, we train an estimator model that express the contribution of a node over the desired prediction.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
deepkashiwa20/Urban_Concept_Drift
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Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
vperifan/federated-time-series-forecasting
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Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation.
Uncertainty-aware Traffic Prediction under Missing Data
However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations.
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.
Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
Traffic prediction is one of the key elements to ensure the safety and convenience of citizens.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
alextimans/traffic4cast-uncertainty
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We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
lmissher/stwave
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IEEE 39th International Conference on Data Engineering (ICDE) 2023
To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied.
