Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

doi:10.1007/s11704-023-2704-x

Front. Comput. Sci.

2023, Vol. 17

Issue (6) : 176615 https://doi.org/10.1007/s11704-023-2704-x

Information Systems

Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

Fangshu CHEN¹, Yufei ZHANG¹, Lu CHEN², Xiankai MENG¹, Yanqiang QI¹, Jiahui WANG¹(

)

¹. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
². College of Computer Science, Zhejiang University, Hangzhou 310027, China

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Corresponding Author(s): Jiahui WANG

Just Accepted Date: 10 October 2023 Issue Date: 29 November 2023

Cite this article:

Fangshu CHEN,Yufei ZHANG,Lu CHEN, et al. Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks[J]. Front. Comput. Sci., 2023, 17(6): 176615.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2704-x
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I6/176615

Fig.1 Framework of DTT-STG

Fig.2 An example of map-matching processing

Fig.3 Comparison of models based on different metrics. (a) Confidence threshold and compression rate; (b) compression and fitting rate

Tab.1 Performance comparison of different approaches and intervals

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