# Traffic Prediction

81 papers with code • 29 benchmarks • 11 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## Most implemented papers

# 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.

# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.

# Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Timely accurate traffic forecast is crucial for urban traffic control and guidance.

# T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

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.

# 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.

# 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.

# 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.

# 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.

# Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.

# SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction

One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.