一种融合纵横时空特征的交通流预测方法

侯越; 郑鑫; 韩成艳

doi:10.19665/j.issn1001-2400.20221101

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一种融合纵横时空特征的交通流预测方法

信息与通信工程 & 计算机科学与技术 | 更新时间：2023-11-30

- 一种融合纵横时空特征的交通流预测方法
- 暂无标题
- 西安电子科技大学学报 2023年50卷第5期页码：65-74
- 作者机构：
  
  兰州交通大学电子与信息工程学院,甘肃兰州 730070
- 作者简介：
  
  [ "侯越(1979—),女,教授,E-mail:[email protected];" ]
  [ "郑鑫(1998—),男,兰州交通大学硕士研究生,E-mail:[email protected];" ]
  [ "韩成艳(1998—),女,兰州交通大学硕士研究生,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62063014);甘肃省自然基金(22JR5RA365);甘肃省教育科技创新项目(2021CYZC-04)
- DOI：10.19665/j.issn1001-2400.20221101
  中图分类号：
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侯越, 郑鑫, 韩成艳. 一种融合纵横时空特征的交通流预测方法[J]. 西安电子科技大学学报, 2023,50(5):65-74.
侯越, 郑鑫, 韩成艳. 一种融合纵横时空特征的交通流预测方法[J]. 西安电子科技大学学报, 2023,50(5):65-74. DOI： 10.19665/j.issn1001-2400.20221101.

DOI：

摘要

针对现有城市道路交通流预测研究中,上下游交通流时滞特性与空间流动特性挖掘不足、车道级交通流时空特性考虑不充分的问题,提出一种融合纵横时空特征的交通流预测方法。首先,通过计算延迟时间量化并消除上下游交通流断面间的空间时滞影响,增强上下游交通流序列的时空相关性。其次,将消除空间时滞的交通流通过向量拆分数据输入方式传入双向长短时记忆网络,用以捕捉上下游交通流纵向的传递与回溯双向时空关系,同时利用多尺度卷积群挖掘待预测断面内部各车道交通流间多时间步横向时空关系。最后,采用注意力机制动态融合纵横时空特征得到预测输出值。实验结果表明,相较于常规时间序列预测模型,所提方法在单步预测实验中,平均绝对误差、均方根误差分别下降了约15.26%、13.83%,决定系数提升了约1.25%。在中长时多步预测实验中,进一步证明了所提方法可有效挖掘纵横向交通流的细粒化时空特征,并具有一定的稳定性和普适性。

Abstract

Aiming at the problems of insufficient mining of time delay characteristics and spatial flow characteristics of upstream and downstream traffic flow as well as insufficient consideration of spatiotemporal characteristics of lane-level traffic flow in existing urban road traffic flow prediction research,a traffic flow prediction method for integrating longitudinal and horizontal spatiotemporal characteristics is proposed.First,the method quantifies and eliminates the effect of spatial time lag between upstream and downstream traffic flow by calculating the delay time to enhance the spatiotemporal correlation of upstream and downstream traffic flow sequences.Then,the traffic flow with the elimination of spatial time lag is passed into the bidirectional long short-term memory network through the vector split data input method to capture the longitudinal transmission and backtracking bidirectional spatiotemporal relationship of upstream and downstream traffic flow.At the same time,the multiscale convolution group is used to mine the multi-time step horizontal spatiotemporal relationship between the traffic flows of each lane in the section to be predicted.Finally,the attention mechanism is used to dynamically fuse the longitudinal and horizontal spatiotemporal characteristics to obtain the predicted value.Experimental results show that by applying the proposed method in the single-step prediction experiment,the MAE and RMSE decrease by 15.26% and 13.83% respectively,and increase by 1.25% compared with conventional time series prediction model.In the medium and long-term multi-step prediction experiment,it is further proved that the proposed method can effectively mine the fine-grained spatiotemporal characteristics of longitudinal and horizontal traffic flow,and has a certain stability and universality.

关键词

城市交通交通流预测纵横时空相关性深度学习特征融合

Keywords

urban transportationtraffic flow predictionlongitudinal and horizontal spatiotemporal correlationdeep learningfeature fusion

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