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2025年4月3日 4:37 星期四
首页 > 过刊浏览>2023年第32卷第4期 >308-316. DOI:10.15888/j.cnki.csa.009076
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面向高速公路通行时间分布预测的时空混合密度神经网络
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中央高校基本科研业务费(2019JBM023)


Spatial-temporal Mixture Density Network for Freeway Travel Time Distribution Prediction
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    摘要:

    准确的通行时间分布预测可以全面地反映高速公路路网中各个路段在未来的通行状况, 辅助实现高速公路中的路径规划, 事故事件预警等精细化管理目标. 为此, 本文提出一种面向高速公路通行时间分布预测的时空混合密度神经网络. 具体地, 本文利用自适应图卷积通过数据驱动的方式提取路网中的空间特征, 有效解决了基于预定义图难以捕获路网信息中完整空间相关性的问题. 在时间维度上, 不同时间的路网信息存在显著的相关性, 因此, 本文基于注意力机制自适应建模路网信息的时间相关性, 并通过卷积层进一步聚合相邻时间步之间的信息. 最后, 基于自适应时空相关性建模得到的路段嵌入表示, 通过混合密度网络建模通行时间的分布, 以实现高速公路中各个路段的通行时间分布预测.

    Abstract:

    Accurate travel time distribution prediction can comprehensively reflect the traffic conditions of each road segment of the freeway network in the future and assist in realizing fine management objectives such as route planning and accident warning on freeways. To this end, this study proposes a spatial-temporal neutral network with mixture density for freeway travel time distribution prediction. Specifically, adaptive graph convolution is used to extract the spatial features of the freeway network in a data-driven way, which effectively solves the problem that it is difficult to capture the complete spatial correlation of freeway network information based on predefined graphs. In the temporal dimension, a significant correlation exists in the freeway network at different times. Therefore, this study adaptively models the temporal correlation of freeway network information based on the attention mechanism and further aggregates the information between adjacent time steps through the convolution layer. Finally, according to the embedding of road segments obtained by adaptive spatio-temporal correlation modeling, the travel time distribution is modeled by a mixture density network to predict the travel time distribution in each road segment of freeways.

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杜渐,段洪琳,王振华,毛潇苇,文言.面向高速公路通行时间分布预测的时空混合密度神经网络.计算机系统应用,2023,32(4):308-316

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  • 收稿日期:2022-09-16
  • 最后修改日期:2022-10-19
  • 在线发布日期: 2023-03-01
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