About
Traffic prediction is the task of predicting traffic volumes, utilising historical speed and volume data.
( Image credit: BaiduTraffic )
Benchmarks
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Subtasks
Greatest papers with code
Sequence to Sequence Learning with Neural Networks
Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
Ranked #5 on
Traffic Prediction
on PeMS-M
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on
Traffic Prediction
on PEMS-BAY
MULTIVARIATE TIME SERIES FORECASTING SPATIO-TEMPORAL FORECASTING TIME SERIES TIME SERIES PREDICTION TRAFFIC PREDICTION
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
Ranked #4 on
Traffic Prediction
on PeMS-M
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• lehaifeng/T-GCN
• 
However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).
AUTONOMOUS VEHICLES TRAFFIC PREDICTION TRAJECTORY PREDICTION
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
• zhiyongc/Graph_Convolutional_LSTM
• 
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
Incrementally Improving Graph WaveNet Performance on Traffic Prediction
We present a series of modifications which improve upon Graph WaveNet's previously state-of-the-art performance on the METR-LA traffic prediction task.
Ranked #2 on
Traffic Prediction
on METR-LA
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #1 on
Traffic Prediction
on PEMS-BAY
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
GMAN: A Graph Multi-Attention Network for Traffic Prediction
Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder.