skip to main content
10.1145/3097983.3097985acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

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

Published:13 August 2017Publication History

ABSTRACT

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 point 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.

Skip Supplemental Material Section

Supplemental Material

wang_dangerous_goods.mp4

mp4

392.7 MB

References

  1. Houda Achour and Mounir Belloumi 2016. Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia. Renewable and Sustainable Energy Reviews Vol. 56 (2016), 988--998. Google ScholarGoogle ScholarCross RefCross Ref
  2. Rakesh Agrawal, Ramakrishnan Srikant, and others. 1994. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, Vol. Vol. 1215. 487--499.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. I Annex 2009. Description of Work, the Sixth Framework Programme, Priority 2-IST, Information Society Technologies. ÆGIS project, CN Vol. 224348 (2009).Google ScholarGoogle Scholar
  4. Mehmet Aldonat Beyzatlar, Müge Karacal, and Hakan Yetkiner. 2014. Granger-causality between transportation and GDP: A panel data approach. Transportation Research Part A: Policy and Practice Vol. 63 (2014), 43--55. Google ScholarGoogle ScholarCross RefCross Ref
  5. Derya Birant and Alp Kut 2007. ST-DBSCAN: An algorithm for clustering spatial--temporal data. Data & Knowledge Engineering Vol. 60, 1 (2007), 208--221. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Juan Gabriel Brida, Martín Alberto Rodríguez-Brindis, and Sandra Zapata-Aguirre 2016. Causality between economic growth and air transport expansion: empirical evidence from Mexico. World Review of Intermodal Transportation Research, Vol. 6, 1 (2016), 1--15. Google ScholarGoogle ScholarCross RefCross Ref
  7. Roberto Bubbico, Sergio Di Cave, and Barbara Mazzarotta. 2004. Risk analysis for road and rail transport of hazardous materials: a simplified approach. Journal of Loss Prevention in the Process Industries, Vol. 17, 6 (2004), 477--482. Google ScholarGoogle ScholarCross RefCross Ref
  8. Sanjay Chawla, Yu Zheng, and Jiafeng Hu 2012. Inferring the root cause in road traffic anomalies 2012 IEEE 12th International Conference on Data Mining. IEEE, 141--150.Google ScholarGoogle Scholar
  9. Martin Ester, Hans Peter Kriegel, Jörg Sander, and Xiaowei Xu 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise International Conference Knowledge Discovery and Data Mining. 226--231.Google ScholarGoogle Scholar
  10. B Fabiano, F Curro, AP Reverberi, and R Pastorino. 2005. Dangerous good transportation by road: from risk analysis to emergency planning. Journal of Loss Prevention in the Process Industries, Vol. 18, 4 (2005), 403--413. Google ScholarGoogle ScholarCross RefCross Ref
  11. Tony H Grubesic, Ran Wei, and Alan T Murray 2014. Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense. Annals of the Association of American Geographers, Vol. 104, 6 (2014), 1134--1156.Google ScholarGoogle ScholarCross RefCross Ref
  12. Jiawei Han, Jian Pei, and Yiwen Yin 2000. Mining frequent patterns without candidate generation ACM Sigmod Record, Vol. Vol. 29. ACM, 1--12.Google ScholarGoogle Scholar
  13. MX Hoang, Y Zheng, and AK Singh 2016. Forecasting citywide crowd flows based on big data. ACM SIGSPATIAL (2016).Google ScholarGoogle Scholar
  14. Liang Hong, Yu Zheng, Duncan Yung, Jingbo Shang, and Lei Zou 2015. Detecting urban black holes based on human mobility data Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 35.Google ScholarGoogle Scholar
  15. Yan Huang, Shashi Shekhar, and Hui Xiong. 2004. Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering, Vol. 16, 12 (2004), 1472--1485. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Yan Huang, Liqin Zhang, and Pusheng Zhang 2008. A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Transactions on Knowledge and data engineering, Vol. 20, 4 (2008), 433--448. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Michael Iacono and David Levinson 2016. Mutual causality in road network growth and economic development. Transport Policy Vol. 45 (2016), 209--217. Google ScholarGoogle ScholarCross RefCross Ref
  18. Bahar Y Kara and Vedat Verter 2004. Designing a road network for hazardous materials transportation. Transportation Science Vol. 38, 2 (2004), 188--196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing 2011. Discovering spatio-temporal causal interactions in traffic data streams Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1010--1018.Google ScholarGoogle Scholar
  20. Kirsi Mukkala and Hannu Tervo 2013. Air transportation and regional growth: which way does the causality run? Environment and Planning A Vol. 45, 6 (2013), 1508--1520. Google ScholarGoogle ScholarCross RefCross Ref
  21. Raymond T. Ng and Jiawei Han 2002. CLARANS: A method for clustering objects for spatial data mining. IEEE transactions on knowledge and data engineering, Vol. 14, 5 (2002), 1003--1016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Bei Pan, Yu Zheng, David Wilkie, and Cyrus Shahabi. 2013. Crowd sensing of traffic anomalies based on human mobility and social media Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 344--353.Google ScholarGoogle Scholar
  23. Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, and Yu Zheng 2011. On mining anomalous patterns in road traffic streams International Conference on Advanced Data Mining and Applications. Springer, 237--251.Google ScholarGoogle Scholar
  24. E Planas, E Pastor, F Presutto, and J Tixier. 2008. Results of the MITRA project: Monitoring and intervention for the transportation of dangerous goods. Journal of hazardous materials Vol. 152, 2 (2008), 516--526. Google ScholarGoogle ScholarCross RefCross Ref
  25. Rudra P Pradhan and Tapan P Bagchi 2013. Effect of transportation infrastructure on economic growth in India: the VECM approach. Research in Transportation Economics Vol. 38, 1 (2013), 139--148. Google ScholarGoogle ScholarCross RefCross Ref
  26. Grant Purdy. 1993. Risk analysis of the transportation of dangerous goods by road and rail. Journal of Hazardous materials Vol. 33, 2 (1993), 229--259. Google ScholarGoogle ScholarCross RefCross Ref
  27. Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki. 2013. Modeling and probabilistic reasoning of population evacuation during large-scale disaster Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1231--1239.Google ScholarGoogle Scholar
  28. Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki 2014. Prediction of human emergency behavior and their mobility following large-scale disaster Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 5--14.Google ScholarGoogle Scholar
  29. Pang-Ning Tan and others 2006. Introduction to data mining. Pearson Education India.Google ScholarGoogle Scholar
  30. Hanghang Tong, Christos Faloutsos, and Jia Y Pan. 2006. Fast Random Walk with Restart and Its Applications IEEE Sixth International Conference on Data Mining, 2006. ICDM'06. IEEE Computer Society, 613--622.Google ScholarGoogle Scholar
  31. Manish Verma. 2011. Railroad transportation of dangerous goods: A conditional exposure approach to minimize transport risk. Transportation research part C: emerging technologies, Vol. 19, 5 (2011), 790--802. Google ScholarGoogle ScholarCross RefCross Ref
  32. Manish Verma and Vedat Verter 2007. Railroad transportation of dangerous goods: Population exposure to airborne toxins. Computers & operations research Vol. 34, 5 (2007), 1287--1303. Google ScholarGoogle ScholarCross RefCross Ref
  33. Junbo Zhang, Yu Zheng, and Dekang Qi 2016natexlaba. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. arXiv preprint arXiv:1610.00081 (2016).Google ScholarGoogle Scholar
  34. Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi 2016natexlabb. DNN-based prediction model for spatio-temporal data Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 92.Google ScholarGoogle Scholar
  35. Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 5, 3 (2014), 38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. 2011. Urban computing with taxicabs. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 89--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Yu Zheng, Huichu Zhang, and Yong Yu 2015. Detecting collective anomalies from multiple spatio-temporal datasets across different domains. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, and others 2003. Semi-supervised learning using gaussian fields and harmonic functions ICML, Vol. Vol. 3. 912--919.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

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

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
          August 2017
          2240 pages
          ISBN:9781450348874
          DOI:10.1145/3097983

          Copyright © 2017 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 13 August 2017

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          KDD '17 Paper Acceptance Rate64of748submissions,9%Overall Acceptance Rate1,133of8,635submissions,13%

          Upcoming Conference

          KDD '24

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader