  Nowadays, with the rapid development of IoT (Internet of Things) and CPS
(Cyber-Physical Systems) technologies, big spatiotemporal data are being
generated from mobile phones, car navigation systems, and traffic sensors. By
leveraging state-of-the-art deep learning technologies on such data, urban
traffic prediction has drawn a lot of attention in AI and Intelligent
Transportation System community. The problem can be uniformly modeled with a 3D
tensor (T, N, C), where T denotes the total time steps, N denotes the size of
the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the
channels of information. According to the specific modeling strategy, the
state-of-the-art deep learning models can be divided into three categories:
grid-based, graph-based, and multivariate time-series models. In this study, we
first synthetically review the deep traffic models as well as the widely used
datasets, then build a standard benchmark to comprehensively evaluate their
performances with the same settings and metrics. Our study named DL-Traff is
implemented with two most popular deep learning frameworks, i.e., TensorFlow
and PyTorch, which is already publicly available as two GitHub repositories
https://github.com/deepkashiwa20/DL-Traff-Grid and
https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to
deliver a useful resource to researchers who are interested in spatiotemporal
data analysis.
