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Research Article

Diagnosing urban traffic anomalies by integrating geographic knowledge and tensor theory

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Article: 2290347 | Received 09 Aug 2023, Accepted 21 Nov 2023, Published online: 15 Dec 2023

References

  • Boyd, S., et al. 2011. “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.” Foundations & Trends® in Machine Learning 3 (1): 1–29. https://doi.org/10.1561/2200000016.
  • Breunig, M. M., Kriegel H. P., Ng R. T., and J. Sander . 2000. LOF: Identifying Density-Based Local Outliers. ed. Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 93–104.
  • Cai, J.-F., E. J. Candès, and Z. Shen. 2010. “A Singular Value Thresholding Algorithm for Matrix Completion.” SIAM Journal on Optimization 20 (4): 1956–1982. https://doi.org/10.1137/080738970.
  • Candès, E. J., X. Li, Y. Ma, and J. Wright. 2011. “Robust Principal Component Analysis?” Journal of the ACM (JACM) 58 (3): 1–37. https://doi.org/10.1145/1970392.1970395.
  • Cao, F., J. Chen, H. Ye, J. Zhao, and Z. Zhou. 2017. “Recovering Low-Rank and Sparse Matrix Based on the Truncated Nuclear Norm.” Neural Networks 85:10–20. https://doi.org/10.1016/j.neunet.2016.09.005.
  • Carroll, J. D., and J.-J. Chang. 1970. “Analysis of Individual Differences in Multidimensional Scaling via an N-Way Generalization of “Eckart-Young” Decomposition.” Psychometrika 35 (3): 283–319. https://doi.org/10.1007/BF02310791.
  • Cheng, X. M., Z. Wang, X. Yang, L. Xu, and Y. Liu. 2021. “Multi-Scale Detection and Interpretation of Spatio-Temporal Anomalies of Human Activities Represented by Time-Series.” Computers Environment and Urban Systems 88:101627. https://doi.org/10.1016/j.compenvurbsys.2021.101627.
  • Chen, X., Z. He, Y. Chen, Y. Lu, and J. Wang. 2019. “Missing Traffic Data Imputation and Pattern Discovery with a Bayesian Augmented Tensor Factorization Model.” Transportation Research Part C: Emerging Technologies 104:66–77. https://doi.org/10.1016/j.trc.2019.03.003.
  • Chen, L., 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): 380–391. https://doi.org/10.1109/THMS.2016.2596103.
  • Chen, X., M. Lei, N. Saunier, and L. Sun. 2022. “Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation.” IEEE Transactions on Intelligent Transportation Systems 23 (8): 12301–12310. https://doi.org/10.1109/TITS.2021.3113608.
  • Chen, X., J. Yang, and S. Sun. 2020. “A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation.” Transportation Research Part C: Emerging Technologies 117:102673. https://doi.org/10.1016/j.trc.2020.102673.
  • Chen, B. Y., H. Yuan, Q. Li, W. H. K. Lam, S.-L. Shaw, and K. Yan. 2014. “Map-Matching Algorithm for Large-Scale Low-Frequency Floating Car Data.” International Journal of Geographical Information Science 28 (1): 22–38. https://doi.org/10.1080/13658816.2013.816427.
  • Fanaee-T, H., and J. Gama. 2016. “Event Detection from Traffic Tensors: A Hybrid Model.” Neurocomputing 203:22–33. https://doi.org/10.1016/j.neucom.2016.04.006.
  • Fang, M., L. Tang, X. Yang, Y. Chen, C. Li, and Q. Li. 2022. “FTPG: A Fine-Grained Traffic Prediction Method with Graph Attention Network Using Big Trace Data.” IEEE Transactions on Intelligent Transportation Systems 23 (6): 5163–5175. https://doi.org/10.1109/TITS.2021.3049264.
  • Harshman, R. A. 1970. “Foundations of the PARAFAC Procedure: Models and Conditions for an“explanatory” Multimodal Factor Analysis.”
  • Hillar, C. J., and L.-H. Lim. 2013. “Most tensor problems are NP-hard.” Journal of the ACM (JACM) 60 (6): 1–39. https://doi.org/10.1145/2512329.
  • Hu, Y., and D. B. Work. 2021. “Robust Tensor Recovery with Fiber Outliers for Traffic Events.” ACM Transactions on Knowledge Discovery from Data 15 (1): 1–27. https://doi.org/10.1145/3417337.
  • Hu, Y., D. Zhang, J. Ye, X. Li, and X. He. 2012. “Fast and accurate matrix completion via truncated nuclear norm regularization.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (9): 2117–2130. https://doi.org/10.1109/TPAMI.2012.271.
  • Jiang, Z., Y. Liu, X. Fan, C. Wang, J. Li, and L. Chen. 2020. “Understanding Urban Structures and Crowd Dynamics Leveraging Large-Scale Vehicle Mobility Data.” Frontiers of Computer Science 14 (5): 1–12. https://doi.org/10.1007/s11704-019-9034-z.
  • Kan, Z., Tang, L., Kwan, M-P., Ren, C., Liu, D., and Li, Q. 2019. “Traffic Congestion Analysis at the Turn Level Using Taxis’ GPS Trajectory Data.” Computers, Environment and Urban Systems 74:229–243. https://doi.org/10.1016/j.compenvurbsys.2018.11.007
  • Kolda, T. G., and B. W. Bader. 2009. “Tensor decompositions and applications.” SIAM Review 51 (3): 455–500. https://doi.org/10.1137/07070111X.
  • Kong, X., B. Zhu, G. Shen, T. C. Workneh, Z. Ji, Y. Chen, Z. Liu, et al. 2021. “Spatial-Temporal-Cost Combination Based Taxi Driving Fraud Detection for Collaborative Internet of Vehicles.” IEEE Transactions on Industrial Informatics 18 (5): 3426–3436. https://doi.org/10.1109/TII.2021.3111536.
  • Li, X., M. K. Ng, G. Cong, Y. Ye, and Q. Wu. 2016. “MR-NTD: Manifold regularization nonnegative tucker decomposition for tensor data dimension reduction and representation.” IEEE Transactions on Neural Networks and Learning Systems 28 (8): 1787–1800. https://doi.org/10.1109/TNNLS.2016.2545400.
  • Lin, C., Q. Zhu, S. Guo, Z. Jin, Y.-R. Lin, and N. Cao. 2018. “Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis.” Data Mining and Knowledge Discovery 32 (4): 1056–1073. https://doi.org/10.1007/s10618-018-0560-3.
  • Liu, J., P. Musialski, P. Wonka, and J. Ye. 2012. “Tensor Completion for Estimating Missing Values in Visual Data.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1): 208–220. https://doi.org/10.1109/TPAMI.2012.39.
  • Liu, F. T., K. M. Ting, and Z.-H. Zhou. 2008. Isolation Forest. 2008 eighth ieee international conference on data mining, Pisa, Italy, December 15–19, 2008, 413–422. IEEE. https://doi.org/10.1109/ICDM.2008.17.
  • Liu, F. T., K. M. Ting, and Z.-H. Zhou. 2012. “Isolation-Based Anomaly Detection.” ACM Transactions on Knowledge Discovery from Data (TKDD) 6 (1): 1–39. https://doi.org/10.1145/2133360.2133363.
  • Liu, J., and J. YE. 2010. “Efficient l1/lq norm regularization.” arXiv preprint arXiv:1009.4766.
  • Liu, Y., Yipeng, Jiani Liu, Zhen Long, and Ce Zhu. 2022. “Tensor Computation.” In Tensor Computation for Data Analysis, 1–17. Cham: Springer International Publishing.
  • Li, Z., Zhi, Yanmin Zhu, Hongzi Zhu, and Minglu Li. Compressive sensing approach to urban traffic sensing. ed. 2011 31st international conference on distributed computing systems, 2011, 889–898.
  • Lu, C., J. Feng, Y. Chen, W. Liu, Z. Lin, and S. Yan. 2020. “Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (4): 925–938. https://doi.org/10.1109/TPAMI.2019.2891760.
  • Lu, X., Y. Wang, and Y. Yuan. 2013. “Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 51 (7): 4009–4018. https://doi.org/10.1109/TGRS.2012.2226730.
  • Lykov, S., and Y. Asakura. 2020. “Anomalous Traffic Pattern Detection in Large Urban Areas: Tensor-Based Approach with Continuum Modeling of Traffic Flow.” International Journal of Intelligent Transportation Systems Research 18 (1): 13–21. https://doi.org/10.1007/s13177-018-0167-5.
  • Pan, B., Yu Zheng, David Wilkie, and Cyrus Shahabi. Crowd Sensing of Traffic Anomalies Based on Human Mobility and Social Media. ed. Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, 2013, 344–353.
  • Peng, X., C. Lu, Z. Yi, and H. Tang. 2016. “Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations.” IEEE Transactions on Neural Networks and Learning Systems 29 (1): 218–224. https://doi.org/10.1109/TNNLS.2016.2608834.
  • Roweis, S. T., and L. K. Saul. 2000. “Nonlinear Dimensionality Reduction by Locally Linear Embedding.” Science 290 (5500): 2323–2326. https://doi.org/10.1126/science.290.5500.2323.
  • Saberi, M., H. Hamedmoghadam, M. Ashfaq, S. A. Hosseini, Z. Gu, S. Shafiei, D. J. Nair, et al. 2020. “A Simple Contagion Process Describes Spreading of Traffic Jams in Urban Networks.” Nature Communications 11 (1): 1–9. https://doi.org/10.1038/s41467-020-15353-2.
  • Schläpfer, M., L. Dong, K. O’Keeffe, P. Santi, M. Szell, H. Salat, S. Anklesaria, et al. 2021. “The Universal Visitation Law of Human Mobility.” Nature 593 (7860): 522–527. https://doi.org/10.1038/s41586-021-03480-9.
  • Schölkopf, B., Bernhard, Robert C. Williamson, Alex Smola, John Shawe-Taylor, and John Platt. 1999. “Support Vector Method for Novelty Detection.” Advances in Neural Information Processing Systems: 12.
  • Sofuoglu, S. E., and S. Aviyente. 2022. “Gloss: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data.” Signal Processing 192:108370. https://doi.org/10.1016/j.sigpro.2021.108370.
  • Tang, L., C. Ren, Z. Liu, and Q. Li. 2017. “A road map refinement method using Delaunay triangulation for big trace data.” ISPRS International Journal of Geo-Information 6 (2): 45. https://doi.org/10.3390/ijgi6020045.
  • Tenenbaum, J. B., V. D. Silva, and J. C. Langford. 2000. “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” Science 290 (5500): 2319–2323. https://doi.org/10.1126/science.290.5500.2319.
  • Thomas, T., W. Weijermars, and E. Van Berkum. 2009. “Predictions of Urban Volumes in Single Time Series.” IEEE Transactions on Intelligent Transportation Systems 11 (1): 71–80. https://doi.org/10.1109/TITS.2009.2028149.
  • Transport, U. P. 2016. “Specification for urban traffic performance evaluation.” In TRANSPORT, edited by U. P, 4–6. Beijing: Standards Press of China.
  • Tucker, L. R. 1966. “Some Mathematical Notes on Three-Mode Factor Analysis.” Psychometrika 31 (3): 279–311. https://doi.org/10.1007/BF02289464.
  • Wang, Q., L. Chen, Q. Wang, H. Zhu, and X. Wang. 2020. “Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion.” IEEE Transactions on Network and Service Management 17 (4): 2677–2689. https://doi.org/10.1109/TNSM.2020.3024932.
  • Wang, H., Haiyang, Xiaming Chen, Siwei Qiang, Honglun Zhang, Yongkun Wang, Jianyong Shi, and Yaohui Jin. Early Warning of City-Scale Unusual Social Event on Public Transportation Smartcard Data. ed. 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016, 188–195.
  • Wang, X., and S. U. N. L. 2021. “Diagnosing Spatiotemporal Traffic Anomalies with Low-Rank Tensor Autoregression.” IEEE Transactions on Intelligent Transportation Systems 22 (12): 7904–7913. https://doi.org/10.1109/TITS.2020.3044466.
  • Wang, Y., J. Peng, Q. Zhao, Y. Leung, X.-L. Zhao, and D. Meng. 2018. “Hyperspectral image restoration via total variation regularized low-rank tensor decomposition.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (4): 1227–1243. https://doi.org/10.1109/JSTARS.2017.2779539.
  • Wang, S., Senzhang, Lifang He, Leon Stenneth, Philip S. Yu, and Zhoujun Li. Citywide Traffic Congestion Estimation with Social Media. ed. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015, 1–10.
  • Wang, J., J. Wu, Z. Wang, F. Gao, and Z. Xiong. 2020. “Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization.” IEEE Transactions on Knowledge and Data Engineering 32 (11): 2269–2283. https://doi.org/10.1109/TKDE.2019.2915231.
  • Wang, X., Y. Wu, D. Zhuang, and L. Sun. 2023. “Low-Rank Hankel Tensor Completion for Traffic Speed Estimation.” IEEE Transactions on Intelligent Transportation Systems 24 (5): 4862–4871. https://doi.org/10.1109/TITS.2023.3247961.
  • Wang, X., Y. Zhang, H. Liu, Y. Wang, L. Wang, and B. Yin. 2018. “An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow.” Journal of Advanced Transportation, 2018 2018:1–12. https://doi.org/10.1155/2018/7191549.
  • Wang, Y., Y. Zhang, X. Piao, H. Liu, and K. Zhang. 2019. “Traffic data reconstruction via adaptive spatial-temporal correlations.” IEEE Transactions on Intelligent Transportation Systems 20 (4): 1531–1543. https://doi.org/10.1109/TITS.2018.2854968.
  • Xue, Z., J. Dong, Y. Zhao, C. Liu, and R. Chellali. 2019. “Low-Rank and Sparse Matrix Decomposition via the Truncated Nuclear Norm and a Sparse Regularizer.” The Visual Computer 35 (11): 1549–1566. https://doi.org/10.1007/s00371-018-1555-1.
  • Xu, M., J. Wu, H. Wang, and M. Cao. 2019. “Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization.” IEEE Transactions on Intelligent Transportation Systems 20 (12): 4704–4713. https://doi.org/10.1109/TITS.2019.2941649.
  • Yu, J., Juan, Qiong YANG, Jian-feng LU, Jian-min HAN, and Hao PENG. 2021. “Advanced Map Matching Algorithms: A Survey and Trends.” Acta Electronica Sinica 49 (9): 1818–1829.
  • Zhang, M. Y., H. Fu, Y. Li, and S. Chen. 2019. “Understanding Urban Dynamics from Massive Mobile Traffic Data.” IEEE Transactions on Big Data 5 (2): 266–278. https://doi.org/10.1109/TBDATA.2017.2778721.
  • Zhang, Z., M. Li, X. Lin, and Y. Wang. 2020. “Network-Wide Traffic Flow Estimation with Insufficient Volume Detection and Crowdsourcing Data.” Transportation Research Part C: Emerging Technologies 121:102870. https://doi.org/10.1016/j.trc.2020.102870.
  • Zhang, M., Mingyang, Tong Li, Hongzhi Shi, Yong Li, and Pan Hui. A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data. ed. IJCAI International Joint Conference on Artificial Intelligence, 2019.
  • Zhang, W., G. Qi, G. Pan, H. Lu, S. Li, and Z. Wu. 2015. “City-Scale Social Event Detection and Evaluation with Taxi Traces.” ACM Transactions on Intelligent Systems and Technology (TIST) 6 (3): 1–20. https://doi.org/10.1145/2700478.
  • Zhao, Z., L. Tang, M. Fang, X. Yang, C. Li, and Q. Li. 2023. Toward Urban Traffic Scenarios and More: A Spatio-Temporal Analysis Empowered Low-Rank Tensor Completion Method for Data Imputation. International Journal of Geographical Information Science 37 (9), 1936–1969. https://doi.org/10.1080/13658816.2023.2234434.
  • Zhao, J., Jie, Chao Chen, Chengwu Liao, Hongyu Huang, Jie Ma, Huayan Pu, Jun Luo, Tao Zhu, and Shilong Wang. 2022. 2F-TP: Learning Flexible Spatiotemporal Dependency for Flexible Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems.
  • Zheng, Y., L. Capra, O. Wolfson, and H. Yang. 2014. “Urban Computing: Concepts, Methodologies, and Applications.” ACM Transactions on Intelligent Systems and Technology (TIST) 5 (3): 1–55. https://doi.org/10.1145/2629592.
  • Zhu, X., and D. S. Guo. 2017. “Urban Event Detection with Big Data of Taxi OD Trips: A Time Series Decomposition Approach.” Transactions in Gis 21 (3): 560–574. https://doi.org/10.1111/tgis.12288.