Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of service of the transportation network. With increasing access to larger datasets of higher resolution, the relevance of deep learning for such tasks is increasing. Several comprehensive survey papers in recent years have summarised the deep learning applications in the transportation domain. However, the system dynamics of the transportation network vary greatly between the non-congested state and the congested state -- thereby necessitating the need for a clear understanding of the challenges specific to congestion prediction. In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion. Recurring and non-recurring congestion are discussed separately. Our survey leads us to uncover inherent challenges and gaps in the current state of research. Finally, we present some suggestions for future research directions as answers to the identified challenges.
翻译:检测、预测和缓解交通拥堵的目的在于提高交通网络的服务水平。随着对更高分辨率的较大数据集的获取机会的增加,深入学习对此类任务的相关性正在增加。近年来,一些全面的调查论文总结了交通领域深层学习应用,然而,交通网络的系统动态在非拥挤状态和拥挤状态之间差异很大,因此有必要明确了解交通拥堵预测所特有的挑战。在这次调查中,我们介绍了在与检测、预测和缓解拥挤有关的任务中深度学习应用的现状。对经常性和非经常性拥堵问题进行了分别讨论。我们的调查引导我们发现当前研究状态中固有的挑战和差距。最后,我们提出了一些关于未来研究方向的建议,作为所查明挑战的答案。