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Discovering urban spatio-temporal structure from time-evolving traffic networks

Jingyuan Wang, Fei Gao, Peng Cui, Chao Li and Zhang Xiong

In Asia-Pacific Web Conference (APWeb'14), pp. 93–104, Springer, 2014. Download

The traffic networks reflect the pulse and structure of a city and shows some dynamic characteristic. Previous research in mining structure from networks mostly focus on static networks and fail to exploit the temporal patterns. In this paper, we aim to solve the problem of discovering the urban spatio-temporal structure from time-evolving traffic networks. We model the time-evolving traffic networks into a 3-order tensor, each element of which indicates the volume of traffic from i-th origin area to j-th destination area in k-th time domain. Considering traffic data and urban contextual knowledge together, we propose a regularized Non-negative Tucker Decomposition (rNTD) method, which discovers the spatial clusters, temporal patterns and relations among them simultaneously. Abundant experiments are conducted in a large dataset collected from Beijing. Results show that our method outperforms the baseline method.

Framework of rNTD
Framework of rNTD

If you find our work is helpful for your research, please kindly consider citing our paper.

 

@inproceedings{wang2014discovering,

  title={Discovering urban spatio-temporal structure from time-evolving traffic networks},

  author={Wang, Jingyuan and Gao, Fei and Cui, Peng and Li, Chao and Xiong, Zhang},

  booktitle={Asia-Pacific Web Conference},

  pages={93--104},

  year={2014},

  organization={Springer}

}