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空间关联特性的关联因素过于复杂且难以量化等问题导致短时交通流预测过于依赖时间关联特性。针对这一问题，提出一种考虑时空关联的深度学习短时交通流预测方法。首先，通过构建同时考虑距离、车流流量相似性和车流速度相似性的空间关联性度量函数，量化目标路段与周边关联道路间的空间关联性。然后，构建内嵌长短时记忆神经元的卷积神经网络模型，利用长短时记忆神经元提取数据间的时间关联性，利用空间关联性度量值及交通数据的卷积传输提取数据间的空间关联性，以实现同时考虑时空关联性的交通流预测。实验结果表明，提出的方法能适应工作日和周末等不同交通流特性条件下的短时预测，且与经典方法相比，预测精度更优，在工作日和周末的预测偏差分别为10.45%和12.35%。

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Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)