《计算机应用研究》|Application Research of Computers

基于注意力的时空神经网络城市区域交通流量预测

Predicting citywide traffic flow using attention-based spatial-temporal neural network

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作者 廖挥若,杨燕
机构 1.西南交通大学 计算机与人工智能学院,成都 611756;2.四川省云计算与智能技术重点实验室,成都 611756
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文章编号 1001-3695(2021)10-009-2935-06
DOI 10.19734/j.issn.1001-3695.2021.02.0054
摘要 可靠的交通流量预测在交通管理和公共安全方面具有重要意义。然而,这也是一件具有挑战性的任务,因为它易受到空间依赖性、时间依赖性以及一些额外因素(天气和突发事件等)的影响。现有的大部分工作只考虑了交通数据的部分属性,导致建模不充分,预测性能不理想。因此,提出了一种新的端到端的深度学习模型——时空注意力卷积长短期记忆网络(ST-AttConvLSTM),用于交通流量的预测。ST-AttConvLSTM将整个模型分为三个分支进行建模,每个分支经过残差神经网络提取局部的空间特征,同时进一步结合天气等外部因素,再利用卷积长短时记忆网络(ConvLSTM)和注意力模型两种组件来挖掘流量的潜在规律,捕获时空维度上数据的关联性。使用北京市和纽约市两个真实的移动数据集来评估提出的方法,实验结果表明,该方法比知名的基准方法有更高的预测精度。
关键词 交通流量预测; 深度学习; 卷积长短时记忆网络; 注意力模型
基金项目 国家自然科学基金资助项目(61976247)
本文URL http://www.arocmag.com/article/01-2021-10-009.html
英文标题 Predicting citywide traffic flow using attention-based spatial-temporal neural network
作者英文名 Liao Huiruo, Yang Yan
机构英文名 1.School of Computing & Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China;2.Key Laboratory of Sichuan Pro-vince for Cloud Computing & Intelligent Technology,Chengdu 611756,China
英文摘要 Reliable traffic flow prediction is of great significance in traffic management and public safety. However, this is also a challenging task because it is affected by spatial dependencies, temporal dependencies and some additional factors(weather and emergencies, etc. ). Most existing works can only consider part of the attributes of traffic data, resulting in insufficient modeling and unsatisfactory prediction performance. This paper proposed a novel end-to-end deep learning model, called spatio-temporal attention ConvLSTM(ST-AttConvLSTM), for traffic flow prediction. ST-AttConvLSTM was divided into three branches for modeling. For each branch, the residual neural network was used to extract local spatial features, and external factors were also combined with them. Then, it employed two components consisting of ConvLSTM and attention model to discover the potential relationship of traffic flow, and capture the correlations of data in both spatial and temporal dimensions. It used two real trips data sets in Beijing and New York to evaluate the proposed method. The experimental results show that this method achieves higher prediction accuracy than well-known baselines.
英文关键词 traffic flow prediction; deep learning; ConvLSTM; attention model
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收稿日期 2021/2/3
修回日期 2021/4/2
页码 2935-2940
中图分类号 TP391
文献标志码 A