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DGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management

Published: 05 May 2021 Publication History

Abstract

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye, to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.

References

[1]
Usman Ali and Tariq Mahmood. 2017. Using deep learning to predict short term traffic flow: A systematic literature review. In Proceedings of the 1st International Conference on Intelligent Transport Systems. Springer, 90–101.
[2]
Derya Birant and Alp Kut. 2007. ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data Knowl. Eng. 60, 1 (2007), 208–221.
[3]
Sanjay Chawla, Yu Zheng, and Jiafeng Hu. 2012. Inferring the root cause in road traffic anomalies. In Proceedings of the IEEE 12th International Conference on Data Mining (ICDM’12). IEEE, 141–150.
[4]
Hongmei Chen, Yixiang Fang, Ying Zhang, Wenjie Zhang, and Lizhen Wang. 2019. ESPM: Efficient spatial pattern matching. IEEE Trans. Knowl. Data Eng. 32, 6 (2019), 1227--1233.
[5]
Ghyzlane Cherradi, Adil EL Bouziri, Azedine Boulmakoul, and Karine Zeitouni. 2017. Real-time microservices based environmental sensors system for Hazmat transportation networks monitoring. Transport. Res. Procedia 27 (2017), 873–880.
[6]
A. Ditta, O. Figueroa, G. Galindo, and R. Yie-Pinedo. 2018. A review on research in transportation of hazardous materials. Socio-Econ. Plan. Sci. 68, 12 (2018), 100665.
[7]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Conference on Knowledge Discovery and Data Mining, Vol. 96. 226–231.
[8]
Edward I. George and Robert E. McCulloch. 1993. Variable selection via Gibbs sampling. J. Amer. Statist. Assoc. 88, 423 (1993), 881–889.
[9]
Tony H. Grubesic, Ran Wei, and Alan T. Murray. 2014. Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense. Ann. Assoc. Amer. Geog. 104, 6 (2014), 1134–1156.
[10]
Samiul Hasan and Satish V. Ukkusuri. 2014. Urban activity pattern classification using topic models from online geo-location data. Transport. Res. Part C: Emerg. Technol. 44 (2014), 363–381.
[11]
Gregor Heinrich. 2005. Parameter Estimation for Text Analysis. Technical Report. Fraunhofer Institute for Computer Graphics Research.
[12]
Henry Hsu and Peter A. Lachenbruch. 2005. Paired t test. Encycl. Biostat. 6 (2005).
[13]
Bo Hu, Mohsen Jamali, and Martin Ester. 2013. Spatio-temporal topic modeling in mobile social media for location recommendation. In Proceedings of the IEEE 13th International Conference on Data Mining. IEEE, 1073–1078.
[14]
Xifei Huang, Xinhao Wang, Jingjing Pei, Ming Xu, Xiaowu Huang, and Yun Luo. 2018. Risk assessment of the areas along the highway due to hazardous material transportation accidents. Nat. Haz. 93, 3 (2018), 1181–1202.
[15]
Yan Huang, Shashi Shekhar, and Hui Xiong. 2004. Discovering colocation patterns from spatial data sets: A general approach. IEEE Trans. Knowl. Data Eng. 16, 12 (2004), 1472–1485.
[16]
Yan Huang, Liqin Zhang, and Pusheng Zhang. 2008. A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20, 4 (2008), 433–448.
[17]
Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, and Ryosuke Shibasaki. 2019. DeepUrbanEvent: A system for predicting citywide crowd dynamics at big events. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2114–2122.
[18]
Masoud Khanmohamadi, Morteza Bagheri, Navid Khademi, and Seyed Farid Ghannadpour. 2018. A security vulnerability analysis model for dangerous goods transportation by rail–Case study: Chlorine transportation in Texas-Illinois. Safety Sci. 110 (2018), 230–241.
[19]
Yaguang Li and Cyrus Shahabi. 2018. A brief overview of machine learning methods for short-term traffic forecasting and future directions. SIGSPATIAL Special 10, 1 (2018), 3–9.
[20]
Yexin Li and Yu Zheng. 2019. Citywide bike usage prediction in a bike-sharing system. IEEE Trans. Knowl. Data Eng. 32, 6 (2019), 1079--1091.
[21]
Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing. 2011. Discovering spatio-temporal causal interactions in traffic data streams. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1010–1018.
[22]
Yu Liu, Martin Ester, Bo Hu, and David W. Cheung. 2015. Spatio-temporal topic models for check-in data. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 889–894.
[23]
Top News. 2016. Guijie comprehensive transformation of start tomorrow. Retrieved from http://www.top-news.top/news-12386939.html.
[24]
Mohammad Noureddine and Milos Ristic. 2019. Route planning for hazardous materials transportation: Multicriteria decision making approach. Dec. Mak.: Applic. Manag. Eng. 2, 1 (2019), 66–85.
[25]
E. Planas, E. Pastor, F. Presutto, and J. Tixier. 2008. Results of the MITRA project: Monitoring and intervention for the transportation of dangerous goods. J. Haz. Mat. 152, 2 (2008), 516–526.
[26]
Francisco Enrique Santarremigia, Gemma Dolores Molero, Sara Poveda-Reyes, and José Aguilar-Herrando. 2018. Railway safety by designing the layout of inland terminals with dangerous goods connected with the rail transport system. Safety Sci. 110 (2018), 206–216.
[27]
Xiaoying Shi, Fanshun Lv, Dewen Seng, Baixi Xing, and Jing Chen. 2019. Exploring the evolutionary patterns of urban activity areas based on origin-destination data. IEEE Access 7 (2019), 20416–20431.
[28]
Ying Sun, Hengshu Zhu, Fuzhen Zhuang, Jingjing Gu, and Qing He. 2018. Exploring the urban region-of-interest through the analysis of online map search queries. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2269–2278.
[29]
Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006. Fast random walk with restart and its applications. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM’06). IEEE, 613–622.
[30]
Jingyuan Wang, Chao Chen, Junjie Wu, and Zhang Xiong. 2017. No longer sleeping with a bomb: A duet system for protecting urban safety from dangerous goods. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1673–1681.
[31]
Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, and Zhang Xiong. 2016. Traffic speed prediction and congestion source exploration: A deep learning method. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM’16). IEEE, 499–508.
[32]
Jingyuan Wang, Xiaoda Wang, Chao Li, Junjie Wu, et al. 2020. Deep fuzzy cognitive maps for interpretable multivariate time series prediction. IEEE Trans. Fuzzy Syst. (2020).
[33]
Jingyuan Wang, Junjie Wu, Ze Wang, Fei Gao, and Zhang Xiong. 2019. Understanding urban dynamics via context-aware tensor factorization with neighboring regularization. IEEE Trans. Knowl. Data Eng. 32, 11 (2019), 2269–2283.
[34]
Jingyuan Wang, Ning Wu, Xinxi Lu, Xin Zhao, and Kai Feng. 2019. Deep trajectory recovery with fine-grained calibration using Kalman filter. IEEE Trans. Knowl. Data Eng. 33, 3 (2019), 921--934.
[35]
Jingyuan Wang, Ning Wu, Wayne Xin Zhao, Fanzhang Peng, and Xin Lin. 2019. Empowering A* search algorithms with neural networks for personalized route recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 539–547.
[36]
Xuerui Wang and Andrew McCallum. 2006. Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 424–433.
[37]
Yuan Wang, Dongxiang Zhang, Ying Liu, Bo Dai, and Loo Hay Lee. 2018. Enhancing transportation systems via deep learning: A survey. Transport. Res. Part C: Emerg. Technol. 99, 2 (2018), 144--163.
[38]
Yingzi Wang, Xiao Zhou, Anastasios Noulas, Cecilia Mascolo, Xing Xie, and Enhong Chen. 2018. Predicting the spatio-temporal evolution of chronic diseases in population with human mobility data. In Proceedings of the International Joint Conference on Artificial Intelligence. 3578–3584.
[39]
Ning Wu, Xin Wayne Zhao, Jingyuan Wang, and Dayan Pan. 2020. Learning effective road network representation with hierarchical graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 6–14.
[40]
Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 186–194.
[41]
Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. 2015. Discovering urban functional zones using latent activity trajectories. IEEE Trans. Knowl. Data Eng. 27, 3 (2015), 712–725.
[42]
Huichu Zhang, Yu Zheng, and Yong Yu. 2018. Detecting urban anomalies using multiple spatio-temporal data sources. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 2, 1 (2018), 54.
[43]
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 38.
[44]
Xiao Zhou, Anastasios Noulas, Cecilia Mascolo, and Zhongxiang Zhao. 2018. Discovering latent patterns of urban cultural interactions in WeChat for modern city planning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1069–1078.
[45]
Xiaojin Zhu, Zoubin Ghahramani, and John D. Lafferty. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning (ICML’03). 912–919.

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  1. DGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management

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      cover image ACM Transactions on Information Systems
      ACM Transactions on Information Systems  Volume 39, Issue 3
      July 2021
      432 pages
      ISSN:1046-8188
      EISSN:1558-2868
      DOI:10.1145/3450607
      Issue’s Table of Contents
      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 the author(s) 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].

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      Publication History

      Published: 05 May 2021
      Accepted: 01 January 2021
      Revised: 01 January 2021
      Received: 01 October 2020
      Published in TOIS Volume 39, Issue 3

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      Author Tags

      1. Urban safety
      2. dangerous goods transportation
      3. risk management
      4. risk pattern
      5. risk causal network

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      • Research-article
      • Refereed

      Funding Sources

      • National Key R&D Program of China
      • National Natural Science Foundation of China
      • Fundamental Research Funds for the Central Universities
      • CCF-DiDi Gaia Collaborative Research Funds for Young Scholars

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