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RADAR: Road Obstacle Identification for Disaster Response Leveraging Cross-Domain Urban Data

Published: 08 January 2018 Publication History

Abstract

Typhoons and hurricanes cause extensive damage to coast cities annually, demanding urban authorities to take effective actions in disaster response to reduce losses. One of the first priority in disaster response is to identify and clear road obstacles, such as fallen trees and ponding water, and restore road transportation in a timely manner for supply and rescue. Traditionally, identifying road obstacles is done by manual investigation and reporting, which is labor intensive and time consuming, hindering the timely restoration of transportation. In this work, we propose RADAR, a low-cost and real-time approach to identify road obstacles leveraging large-scale vehicle trajectory data and heterogeneous road environment sensing data. First, based on the observation that road obstacles may cause abnormal slow motion behaviors of vehicles in the surrounding road segments, we propose a cluster direct robust matrix factorization (CDRMF) approach to detect road obstacles by identifying the collective anomalies of slow motion behaviors from vehicle trajectory data. Then, we classify the detected road obstacles leveraging the correlated spatial and temporal features extracted from various road environment data, including satellite images and meteorological records. To address the challenges of heterogeneous features and sparse labels, we propose a semi-supervised approach combining co-training and active learning (CORAL). Real experiments on Xiamen City show that our approach accurately detects and classifies the road obstacles during the 2016 typhoon season with precision and recall both above 90%, and outperforms the state-of-the-art baselines.

References

[1]
G. Barbarosoglu and Y. Arda. 2004. A Two-Stage Stochastic Programming Framework for Transportation Planning in Disaster Response. Journal of the Operational Research Society 55, 1 (2004), 43--53.
[2]
Avrim Blum and Tom Mitchell. 1998. Combining Labeled and Unlabeled Data with Co-Training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT‘98). ACM, 92--100.
[3]
Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright. 2011. Robust Principal Component Analysis? J. ACM 58, 3 (2011), 1:37.
[4]
Pablo Samuel Castro, Daqing Zhang, Chao Chen, Shijian Li, and Gang Pan. 2013. From Taxi GPS Traces to Social and Community Dynamics: A Survey. ACM Computer Survey 46, 2 (2013), 17:1--17:34.
[5]
Centre for Research on the Epidemiology of Disasters. 2015. The Human Cost of Natural Disasters 2015: A Global Perspective. Technical Report. UN Office for Disaster Risk Reduction, Geneva, Switzerland.
[6]
P. K. Chan and M. V. Mahoney. 2005. Modeling Multiple Time Series for Anomaly Detection. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM‘05). ACM, 8--16.
[7]
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly Detection: A Survey. ACM Computer Survey 41, 3 (2009), 1--58.
[8]
Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 1--27.
[9]
Stephanie E Chang and Nobuoto Nojima. 2001. Measuring Post-Disaster Transportation System Performance: The 1995 Kobe Earthquake in Comparative Perspective. Transportation Research Part A: Policy and Practice 35, 6 (2001), 475--494.
[10]
C. Chen, D. Zhang, P.S. Castro, N. Li, L. Sun, S. Li, and Z. Wang. 2013. iBOAT: Isolation-Based Online Anomalous Trajectory Detection. IEEE Transactions on Intelligent Transportation Systems 14, 2 (2013), 806--818.
[11]
Chao Chen, Daqing Zhang, Nan Li, and Zhi-Hua Zhou. 2014. B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces. IEEE Transactions on Intelligent Transportation Systems 15, 4 (2014), 1451--1465.
[12]
C. Chen, D. Zhang, X. Ma, B. Guo, L. Wang, Y. Wang, and E. Sha. 2017. Crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2017), 1478--1496.
[13]
L. Chen, J. Jakubowicz, D. Yang, D. Zhang, and G. Pan. 2017. Fine-Grained Urban Event Detection and Characterization Based on Tensor Cofactorization. IEEE Transactions on Human-Machine Systems 47, 3 (2017), 380--391.
[14]
Longbiao Chen, Xiaojuan Ma, Thi-Mai-Trang Nguyen, Gang Pan, and Jérémie Jakubowicz. 2016. Understanding Bike Trip Patterns Leveraging Bike Sharing System Open Data. Frontiers of Computer Science (2016), 1--11.
[15]
L. Chen, D. Zhang, X. Ma, L. Wang, S. Li, Z. Wu, and G. Pan. 2016. Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data. IEEE Transactions on Intelligent Transportation Systems 17, 5 (2016), 1227--1242.
[16]
Longbiao Chen, Daqing Zhang, Gang Pan, Xiaojuan Ma, Dingqi Yang, Kostadin Kushlev, Wangsheng Zhang, and Shijian Li. 2015. Bike Sharing Station Placement Leveraging Heterogeneous Urban Open Data. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Vol. UbiComp‘15. ACM, 571--575.
[17]
Longbiao Chen, Daqing Zhang, Leye Wang, Dingqi Yang, Xiaojuan Ma, Shijian Li, Zhaohui Wu, Gang Pan, Thi-Mai-Trang Nguyen, and Jérémie Jakubowicz. 2016. Dynamic Cluster-Based Over-Demand Prediction in Bike Sharing Systems. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp‘16). ACM, 841--852.
[18]
L. Du, X. Li, and Y. D. Shen. 2012. Robust Nonnegative Matrix Factorization via Half-Quadratic Minimization. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM‘12). IEEE, 201--210.
[19]
H. Eren, S. Makinist, E. Akin, and A. Yilmaz. 2012. Estimating Driving Behavior by a Smartphone. In Proceedings of the IEEE Intelligent Vehicles Symposium. IEEE, 234--239.
[20]
Levi Ewan, Ahmed Al-Kaisy, and David Veneziano. 2013. Remote Sensing of Weather and Road Surface Conditions. Transportation Research Record: Journal of the Transportation Research Board 2329 (July 2013), 8--16.
[21]
Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2014. CitySpectrum: A Non-Negative Tensor Factorization Approach. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp‘14). ACM, 213--223.
[22]
Zipei Fan, Xuan Song, Ryosuke Shibasaki, and Ryutaro Adachi. 2015. CityMomentum: An Online Approach for Crowd Behavior Prediction at a Citywide Level. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ‘15). ACM, 559--569.
[23]
Tom Fawcett. 2006. An Introduction to ROC Analysis. Pattern Recognition Letters 27, 8 (2006), 861--874.
[24]
FEMA. 2007. Public Assistance: Debris Management Guide. Technical Report. Federal Emergency Management Agency, Washington, DC.
[25]
Nir Friedman, Dan Geiger, and Moises Goldszmidt. 1997. Bayesian Network Classifiers. Machine Learning 29, 2--3 (1997), 131--163.
[26]
G. H. Golub and C. Reinsch. 1970. Singular Value Decomposition and Least Squares Solutions. Numer. Math. 14, 5 (1970), 403--420.
[27]
B. Guo, C. Chen, D. Zhang, Z. Yu, and A. Chin. 2016. Mobile Crowd Sensing and Computing: When Participatory Sensing Meets Participatory Social Media. IEEE Communications Magazine 54, 2 (2016), 131--137.
[28]
Ying Hu and Guizhong Liu. 2015. Separation of Singing Voice Using Nonnegative Matrix Partial Co-Factorization for Singer Identification. IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, 4 (2015), 643--653.
[29]
Jin Huang, Feiping Nie, Heng Huang, and Chris Ding. 2014. Robust Manifold Nonnegative Matrix Factorization. ACM Transactions on Knowledge Discovery from Data 8, 3 (2014), 1--21.
[30]
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM International Conference on Multimedia (MM‘14). ACM, 675--678.
[31]
Svetlana Kiritchenko and Stan Matwin. 2011. Email Classification with Co-Training. In Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research (CASCON ‘11). IBM Corp., 301--312.
[32]
T. Kolda and B. Bader. 2009. Tensor Decompositions and Applications. SIAM Rev. 51, 3 (2009), 455--500.
[33]
Nicholas D. Lane, Petko Georgiev, and Lorena Qendro. 2015. DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp‘15). ACM, 283--294.
[34]
Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, and Beat Flepp. 2005. Off-Road Obstacle Avoidance Through End-to-End Learning. In Proceedings of the 18th International Conference on Neural Information Processing Systems (NIPS‘05). 739--746.
[35]
G. Lefaix, T. Marchand, and P. Bouthemy. 2002. Motion-Based Obstacle Detection and Tracking for Car Driving Assistance. In Proceedings of the 16th International Conference on Pattern Recognition, Vol. 4. IEEE, 74--77.
[36]
Jing Lei. 2014. Classification with Confidence. Biometrika 101, 4 (2014), 755--769.
[37]
Bin Li, Daqing Zhang, Lin Sun, Chao Chen, Shijian Li, Guande Qi, and Qiang Yang. 2011. Hunting or Waiting? Discovering Passenger-Finding Strategies from a Large-Scale Real-World Taxi Dataset. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom‘11 Workshops). IEEE, 63--68.
[38]
David A. McEntire. 2014. Disaster Response and Recovery: Strategies and Tactics for Resilience. John Wiley 8 Sons.
[39]
Kamal Nigam and Rayid Ghani. 2000. Analyzing the Effectiveness and Applicability of Co-Training. In Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM ‘00). ACM, 86--93.
[40]
G. Pan, G. Qi, Z. Wu, D. Zhang, and S. Li. 2013. Land-Use Classification Using Taxi GPS Traces. IEEE Transactions on Intelligent Transportation Systems 14, 1 (2013), 113--123.
[41]
Thomas A. Ranney. 1994. Models of Driving Behavior: A Review of Their Evolution. Accident Analysis 8 Prevention 26, 6 (1994), 733--750.
[42]
Neil Rubens, Mehdi Elahi, Masashi Sugiyama, and Dain Kaplan. 2015. Active Learning in Recommender Systems. In Recommender Systems Handbook. Springer, 809--846.
[43]
N. Rusli, M. R. Majid, and A. H. M. Din. 2014. Google Earth's Derived Digital Elevation Model: A Comparative Assessment with Aster and SRTM Data. IOP Conference Series: Earth and Environmental Science 18, 1 (2014), 12--65.
[44]
Jörg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. 1998. Density-Based Clustering in Spatial Databases: The Algorithm Gdbscan and Its Applications. Data Mining and Knowledge Discovery 2, 2 (1998), 169--194.
[45]
Burr Settles. 2010. Active Learning Literature Survey. Technical Report. University of Wisconsin.
[46]
B. Shen, B. D. Liu, Q. Wang, and R. Ji. 2014. Robust Nonnegative Matrix Factorization via L1 Norm Regularization by Multiplicative Updating Rules. In Proceedings of the IEEE International Conference on Image Processing (ICIP‘14). 5282--5286.
[47]
P. Sprechmann, A. M. Bronstein, and G. Sapiro. 2015. Learning Efficient Sparse and Low Rank Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9 (2015), 1821--1833.
[48]
Nathan Srebro and Adi Shraibman. 2005. Rank, Trace-Norm and Max-Norm. In Proceedings of the 18th Annual Conference on Learning Theory (COLT‘05). Springer, 545--560.
[49]
Dilek Tuzun Aksu and Linet Ozdamar. 2014. A Mathematical Model for Post-Disaster Road Restoration: Enabling Accessibility and Evacuation. Transportation Research Part E: Logistics and Transportation Review 61 (2014), 56--67.
[50]
Leye Wang, Daqing Zhang, Animesh Pathak, Chao Chen, Haoyi Xiong, Dingqi Yang, and Yasha Wang. 2015. CCS-TA: Quality-Guaranteed Online Task Allocation in Compressive Crowdsensing. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, New York, NY, USA, 683--694.
[51]
Kathleen L. Wolf. 2006. Urban Trees and Traffic Safety: Considering the U.S. Roadside Policy and Crash Data. Arboriculture and Urban Forestry 32, 4 (2006), 170--179.
[52]
M. Xie, L. Trassoudaine, J. Alizon, and J. Gallice. 1994. Road Obstacle Detection and Tracking by an Active and Intelligent Sensing Strategy. Machine Vision and Applications 7, 3 (1994), 165--177.
[53]
H. Xiong, D. Zhang, G. Chen, L. Wang, V. Gauthier, and L. E. Barnes. 2016. iCrowd: Near-Optimal Task Allocation for Piggyback Crowdsensing. IEEE Transactions on Mobile Computing 15, 8 (2016), 2010--2022.
[54]
L. Xiong, X. Chen, and J. Schneider. 2011. Direct Robust Matrix Factorizatoin for Anomaly Detection. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM‘11). 844--853.
[55]
Jar-Ferr Yang and Chiou-Liang Lu. 1995. Combined Techniques of Singular Value Decomposition and Vector Quantization for Image Coding. IEEE Transactions on Image Processing 4, 8 (1995), 1141--1146.
[56]
Y. Yu, J. Li, H. Guan, C. Wang, and C. Wen. 2016. Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data. IEEE Transactions on Intelligent Transportation Systems 17, 12 (2016), 3391--3406.
[57]
Daqing Zhang, Bin Guo, and Zhiwen Yu. 2011. The Emergence of Social and Community Intelligence. Computer 44, 7 (2011), 21--28.
[58]
Minging Zhang, Chao Chen, Tianyu Wo, Tao Xie, Md Zakirul Alam Bhuiyan, and Xuelian Lin. 2017. SafeDrive: Online Driving Anomaly Detection From Large-Scale Vehicle Data. IEEE Transactions on Industrial Informatics 13, 4 (2017), 2087--2096.
[59]
Yihao Zhang, Junhao Wen, Xibin Wang, and Zhuo Jiang. 2014. Semi-Supervised Learning Combining Co-Training with Active Learning. Expert Systems with Applications 41, 5 (2014), 2372--2378.
[60]
Yu Zheng. 2015. Trajectory Data Mining: An Overview. ACM Trans. Intell. Syst. Technol. 6, 3 (2015), 1--41.
[61]
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban Computing: Concepts, Methodologies, and Applications. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 1--55.
[62]
Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-Air: When Urban Air Quality Inference Meets Big Data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘13). ACM, 1436--1444.
[63]
Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. 2011. Urban Computing with Taxicabs. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp‘11). ACM, 89--98.
[64]
Yu Zheng, Huichu Zhang, and Yong Yu. 2015. Detecting Collective Anomalies from Multiple Spatio-Temporal Datasets Across Different Domains. In Proceedings of the ACM International Conference on Advances in Geographic Information Systems. ACM, 1--10.

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 4
    December 2017
    1298 pages
    EISSN:2474-9567
    DOI:10.1145/3178157
    Issue’s Table of Contents
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    Publication History

    Published: 08 January 2018
    Accepted: 01 October 2017
    Received: 01 August 2017
    Published in IMWUT Volume 1, Issue 4

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    Author Tags

    1. Mobility data mining
    2. cross-domain data
    3. disaster response
    4. urban computing

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