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首页> 外文期刊>Intelligent Transport Systems, IET >Spatio-temporal expand-and-squeeze networks for crowd flow prediction in metropolis
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Spatio-temporal expand-and-squeeze networks for crowd flow prediction in metropolis

机译：用于大都市人群流量预测的时空扩展和挤压网络

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摘要

The use of deep learning methods to predict traffic flow in transportation systems has become a hot research project. The existing predictive model method faces problems such as long calculation time and difficult data pre-processing, especially for the prediction effect of high traffic area. In this study, the authors propose a novel framework ST-ESNet, spatio-temporal expand-and-squeeze networks, that designs several effective strategies for considering the complexity, non-linearity and uncertainty of traffic flow, and better captures traffic flow characteristics to adapt to the dynamic characteristics of traffic trajectory, traffic duration and traffic flow. Specially, we use extend-and-squeeze process rather than squeeze-and-extend process during the normal residual unit to capture farther spatial dependence among regions. Specifically, inverted residual and deformed convolution structures are utilised in the expanding process, and the convolution with stride 2 is utilised in the squeeze process. Furthermore, image feature scaling is used in each residual unit to obtain more fine-grained surface information, which improves the ability of the model to capture dynamic spatial dependence features. Finally, they use stochastic weight averaging to obtain an integration model. In summary, they propose a new predictive model ST-ESNet. The experimental results show that the authors' proposed network model has better prediction performance compared with the state-of-the-art model.
机译：使用深度学习方法来预测运输系统中交通流量已成为一个热门研究项目。现有的预测模型方法面临诸如长计算时间和困难数据预处理的问题，特别是对于高流量区域的预测效果。在这项研究中，作者提出了一种新颖的框架ST-ESNet，时空扩展和挤压网络，用于考虑到交通流量的复杂性，非线性和不确定度，更好地捕获交通流特性适应交通轨迹，交通持续时间和流量的动态特征。特别是，在正常的残余单元中，我们使用延伸和挤压过程而不是挤压和延长过程，以捕获区域之间的进一步空间依赖性。具体地，在扩展过程中使用倒置残余和变形卷积结构，并且在挤压过程中使用具有步幅2的卷积。此外，在每个残差单元中使用图像特征缩放以获得更细粒度的表面信息，这提高了模型捕获动态空间依赖特征的能力。最后，它们使用随机重量平均来获得集成模型。总之，他们提出了一个新的预测模型ST-ESNet。实验结果表明，与最先进的模型相比，作者提出的网络模型具有更好的预测性能。

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