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.

中文翻译：

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时空扩展和挤压网络，用于大都市中的人群流量预测

使用深度学习方法预测交通系统中的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着计算时间长，数据预处理困难等问题，特别是对于高流量区域的预测效果。在这项研究中，作者提出了一个新颖的ST-ESNet框架，即时空扩展和压缩网络，该框架设计了几种有效的策略来考虑交通流的复杂性，非线性和不确定性，并更好地捕获交通流的特征，从而适应交通轨迹，交通持续时间和交通流量的动态特性。特别地，我们在正常残差单元中使用扩展和挤压过程，而不是挤压和扩展过程，以捕获区域之间更远的空间依赖性。 。具体地，在膨胀过程中利用倒置的残余和变形的卷积结构，而在压缩过程中利用具有步幅2的卷积。此外，在每个残差单元中使用图像特征缩放以获取更多细粒度的表面信息，从而提高了模型捕获动态空间相关特征的能力。最后，他们使用随机加权平均来获得积分模型。总之，他们提出了一种新的预测模型ST-ESNet。实验结果表明，与最新模型相比，作者提出的网络模型具有更好的预测性能。