Is Classical LSTM more Efficient than Modern GCN Approaches in the Context of Traffic Forecasting?

Haroun Bouchemoukha, Mohamed Nadjib,Zennir Atidel Lahoulou

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)（2021）

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摘要

Traffic forecasting is one of the most difficult challenges in the area of ITS (intelligent transportation systems) because of complex spatial correlations on road networks and non-linear temporal dynamics of changing road conditions. To address these issues, researchers proposed models that combine GCNs (Graph Convolution Networks) and RNNs (Recurrent Neural Networks), in order to inherit the adv...

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关键词

RNN (Recurrent Neural Network),LSTM (Long Short-term Memory),CNN (Convolutional Neural Network),GCN (Graph Convolution Network),Traffic forecasting,Spatialtemporal dependency

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