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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 timeseries 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.

Layered double hydroxide (LDH) has shown great promise for oxygen evolution reaction (OER) in an alkaline solution, but its catalytic performance is still hindered by inadequate active sites and large overpotentials. Here, we report a robust solvothermal reconstruction strategy that can induce rich cavities and active sites in fluorinated NiFe LDH (NiFe-F) electrocatalysts for excellent OER activities. In the process of reconstruction, partial fluoride ions and metal cations (Fe3+) are leached from the catalyst in the alkaline solution at an elevated temperature. This leads to an effective bulk and surface reconstruction, drastically increasing the electrochemical active surface area and exposing more catalytic active sites, therefore improving the kinetics of the reaction and optimizing the binding energy of OER intermediates, as evidenced in our systematic electrochemistry studies and the density functional theory calculations. The reconstructed NiFe-F catalyst exhibits an unprecedented high activity toward OER among the non-noble metal catalysts, with a low overpotential (η) of 152 mV at 10 mA cm–2, high turnover frequency of 1.6 × 10–1 s–1 at η = 200 mV, and small activation energy of 12.8 kJ mol–1 at 1.23 VRHE, and excellent stability in the chronopotentiometry at 10 mA cm–2 for 100 h. This study offers a robust reconstruction strategy toward OER catalyst design for the next-generation clean energy conversion.

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