论文标题
深层神经网络的短期交通预测：一项调查
Short-term Traffic Prediction with Deep Neural Networks: A Survey
论文作者
论文摘要
在现代运输系统中，每天都会生成大量的流量数据。这导致了短期交通预测（STTP）的快速进步，最近采用了深度学习方法。在具有复杂时空关系的交通网络中，深度神经网络（DNN）通常表现良好，因为它们能够自动提取最重要的功能和模式。在这项研究中，我们调查了最近从四个角度应用深网的STTP研究。 1）我们根据所涉及的空间和时间依赖的数量和类型来汇总输入数据表示方法。 2）我们简要解释了从最早的网络（包括受限制的玻尔兹曼机器）到最近的最新网络，包括基于图和元学习网络的最新网络。 3）我们根据DNN技术，应用区域，数据集和代码可用性以及代表时空依赖性的类型总结了先前的STTP研究。 4）我们编译了流行的公共流量数据集，可以用作标准基准。最后，我们建议在STTP中具有挑战性的问题以及可能的未来研究指示。
In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.
