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Smart city article details
| Title | Uctb: Spatiotemporal Crowd Flow Prediction Toolbox |
|---|---|
| ID_Doc | 59340 |
| Authors | Chen L.; Chai D.; Wang L. |
| Year | 2022 |
| Published | Journal of Frontiers of Computer Science and Technology, 16, 4 |
| DOI | http://dx.doi.org/10.3778/j.issn.1673-9418.2012072 |
| Abstract | Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. There are mainly two major shortcomings that plague researchers and developers. Firstly, crowd flow is affected by complex factors and previous studies have summarized a variety of spatiotemporal prior knowledge. However, it is difficult for follow-up work to comprehensively incorporate this prior knowledge as its application scenarios are diverse. Secondly, with the development of deep learning technology, implementing state-of-the-art models is cumbersome work and becomes more and more complicated. To fill in the above gaps, this paper designs time-series sampling interfaces and graph construction interfaces. The time series sampling interfaces can generate different types of time series based on diverse prior knowledge, and the graph construction interfaces can build different types of spatial graphs. Moreover, users can extend the above two interfaces to utilize new spatiotemporal prior knowledge. Based on the TensorFlow framework, this paper implements a variety of advanced spatiotemporal graph models and encapsulates the frequently-used spatiotemporal modeling units. Users can leverage state-of-the-art spatiotemporal models and perform customized development based on these advanced layers. In summary, the spatiotemporal crowd flow prediction tool box UCTB integrates diverse spatiotemporal prior knowledge and a variety of advanced models, which may promote the development of spatiotemporal crowd flow prediction applications. The codes and detailed documents are open-source. The URL of UCTB is https://github.com/uctb/UCTB. © 2022, Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved. |
| Author Keywords | crowd flow; open-source toolbox; spatiotemporal prediction; urban computing |
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