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Traffic Prediction

6 papers with code · Time Series

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

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Greatest papers with code

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.


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.


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

20 Feb 2018zhiyongc/Graph_Convolutional_LSTM

The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.


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.