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Using Convolutional Neural Network with Asymmetrical ...
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由 D Zang 著作2017被引用 13 次 — In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway.
Using Convolutional Neural Network with Asymmetrical ...
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由 D Zang 著作2017被引用 13 次 — In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway.
Using Convolutional Neural Network with Asymmetrical ...
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由 D Zang 著作2017被引用 13 次 — In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway.
Using Convolutional Neural Network with Asymmetrical Kernels ...
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In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway.
Using Convolutional Neural Network with Asymmetrical Kernels ...
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Bibliographic details on Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway.
Search - Intelligence Science I (ICIS 2017)
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Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway · Traffic Parameters Prediction Using a Three-Channel ...
Short-Term travel speed prediction for urban expressways ...
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由 T Han 著作2020被引用 3 次 — We developed a multi-dimensional learning machine for predicting the traffic speed. Proposed methodology considered both historical experience and near past ...
Keshuang Tang
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Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway. ... Using a Three-Channel Convolutional Neural Network ...
Jiujun Cheng's research works | Tongji University and other places
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In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn ...
Lane‐level short‐term travel speed prediction for urban ...
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由 K Tang 著作2024被引用 2 次 — This study proposes a three-dimensional (3D) dual attention convolution-based deep learning model for predicting the lane-level travel speed.