GAMIT : A New Encoder-Decoder Framework with Graphical Space and Multi-grained Time for Traffic Predictions

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data
EditorsXintao Wu, Chris Jermaine, Li Xiong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages938-943
ISBN (Print)9781728162515
Publication statusPublished - Dec 2020

Publication series

NameProceedings - IEEE International Conference on Big Data, Big Data

Conference

Title8th IEEE International Conference on Big Data (Big Data 2020)
LocationVirtual
PlaceUnited States
CityAtlanta
Period10 - 13 December 2020

Abstract

Nowadays, many researchers study on characterizing complex and dynamic traffic environments by modeling the spatio-temporal dependencies in a road network for traffic predictions. However, existing works fail to investigate comprehensive spatio-temporal dependencies, because most of them ignore the spatial dependencies from the topological graph structure information in the road network, or only consider the temporal dependencies between fine-grained time slots whereas ignore those among coarse-grained time periods, in which a time period is composed of a number of time slots. To this end, we propose a new encoder-decoder framework called GAMIT with graphical space and multi-grained time by developing a spatiotemporal recurrent neural network (STRNN) for traffic predictions, where the graphical space consists of spatial networks which represent road networks. STRNN first devises a spatiotemporal convolution block to capture the fine-grained spatiotemporal dependencies between time slots in the road network. Then, STRNN uses the recurrent architecture to catch the coarsegrained spatio-temporal dependencies among time periods. The encoder finally applies STRNN to learn the multi-grained spatiotemporal dependencies which are fed into the decoder for computing traffic predictions based on STRNN as well. To evaluate the performance of GAMIT, we conduct extensive experiments on two real traffic flow datasets. Experimental results show that GAMIT outperforms the state-of-the-art traffic prediction models.

Research Area(s)

  • graphical space, multi-grained time, spatio-temporal dependencies, Traffic predictions

Citation Format(s)

GAMIT : A New Encoder-Decoder Framework with Graphical Space and Multi-grained Time for Traffic Predictions. / He, Zhixiang; Chow, Chi-Yin; Zhang, Jia-Dong.

Proceedings - 2020 IEEE International Conference on Big Data. ed. / Xintao Wu; Chris Jermaine; Li Xiong. Institute of Electrical and Electronics Engineers Inc., 2020. p. 938-943 9378143 (Proceedings - IEEE International Conference on Big Data, Big Data).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review