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No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods

Jingyuan Wang, Chao Chen, Junjie Wu and Zhang Xiong

In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'17), pp. 1673–1681, ACM, 2017. Download

Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly “bomb” we sleep with every day. In both academia and government, tremendous efforts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the problem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features a “duet” between DGT trajectory data and human mobility data for risky zones identification. Moreover, DGeye innovatively takes risky patterns as the keystones in DGT management, and builds causality networks among them for pain points identification, attribution and prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye. In particular, the report generated by DGeye driven the Beijing government to lay down gas pipelines for the famous Guijie food street. 

Framework of DGeye
Framework of DGeye
Spatial distributions of crowd weights, DGT weights, and risky zones in Beijing and Tianjin
Spatial distributions of crowd weights, DGT weights, and risky zones in Beijing and Tianjin

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

 

@inproceedings{wang2017no,

  title={No longer sleeping with a bomb: a duet system for protecting urban safety from dangerous goods},

  author={Wang, Jingyuan and Chen, Chao and Wu, Junjie and Xiong, Zhang},

  booktitle={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},

  pages={1673--1681},

  year={2017},

  organization={ACM}

}