Transportation network speed foreeasting method using deep capsule networks with nested LSTM models

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United States of America

PATENT NO 11270579
APP PUB NO 20200135017A1
SERIAL NO

16385302

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This application is a transportation network speed forecasting method using deep capsule networks with nested LSTM models. The method includes the following steps: (1) This method divides the transport network into road links, calculates average speeds of each road link, maps the average speeds into a grid system, and generate traffic images representing traffic state at time intervals; (2) the method uses a CapsNet to capture the spatial relationship between road links. The learn patterns are represented in vectors; (3) The vectors of CapsNet are feed into a NLSTM model to learn temporal relationships between road links; (4) The model is trained using and training dataset, and predicts future traffic states using testing dataset. This application uses a new and advanced CapsNet neural structure, while can more efficiently deal with complex traffic networks than CNN models.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Li, Yi Beijing, CN 871 4882
Luan, Sen Beijing, CN 1 1
Ma, Xiaolei Beijing, CN 4 1
Wang, Yunpeng Beijing, CN 19 45
Zhuang, Dai Beijing, CN 1 1

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