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Traffic Prediction

10 papers with code · Time Series

Traffic prediction is the task of predicting traffic volumes, utilising historical speed and volume data.

( Image credit: BaiduTraffic )

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Greatest papers with code

Sequence to Sequence Learning with Neural Networks

NeurIPS 2014 farizrahman4u/seq2seq

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.

MACHINE TRANSLATION TRAFFIC PREDICTION

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

12 Nov 2018lehaifeng/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.

TRAFFIC PREDICTION

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

13 Jun 2018JingqingZ/BaiduTraffic

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

TRAFFIC PREDICTION

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

3 Mar 2018tangxianfeng/STDN

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.

TRAFFIC PREDICTION

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

20 Feb 2018zhiyongc/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.

TRAFFIC PREDICTION

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

31 May 2019nnzhan/Graph-WaveNet

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.

TRAFFIC PREDICTION

GMAN: A Graph Multi-Attention Network for Traffic Prediction

11 Nov 2019zhengchuanpan/GMAN

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.

TRAFFIC PREDICTION

Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

26 Jan 2019Lemma1/DPFE

A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem.

TRAFFIC PREDICTION