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Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics

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Abstract

Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal evolution of traffic flows and find the traffic anomalies. This method roots in the ideal of global traffic flow network detection. Therefore, it is more informative than local statistics since traffic flows evolve in a mutually interacting manner to spread out all over the city. The experimental results confirm its predictive power in detecting spatiotemporal traffic anomalies.

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References

  1. Agovic A, Banerjee A, Ganguly AR, Protopopescu V (2009) Anomaly detection using manifold embedding and its applications in transportation corridors. Intell Data Anal 13:435–455

    Article  Google Scholar 

  2. Chandra SR, Al-Deek H (2009) Predictions of freeway traffic sand volumes using vector autoregressive models. J Intell Transp Syst 13:53–72

    Article  Google Scholar 

  3. Chen S, Wang W, van Zuylen H (2010) A comparison of outlier detection algorithms for ITS data. Expert Syst Appl 37:1169–1178

    Article  Google Scholar 

  4. Chen L, Yang D, Jakubowicz J, Pan G, Zhang D, Li S (2016) Sensing the pulse of urban activity centers leveraging bike sharing open data. In: Ubiquitous Intelligence and Computing and 2015 IEEE Intl Conf on Autonomic and Trusted Computing and 2015 IEEE Intl Conf on Scalable Computing and Communications and ITS Associated Workshops. pp 135–142

  5. Daneshfar F, Ravanjamjah J, Mansoori F, Bevrani H, Azami BZ (2009) Adaptive fuzzy urban traffic flow control using a cooperative multi-agent system based on two stage fuzzy clustering. In: Vehicular Technology Conference, Vtc Spring 2009. IEEE, pp. 1–5

  6. Daraghmi YA, Yi CW, Chiang TC (2014) Negative binomial additive models for short-term traffic flow forecasting in urban areas IEEE Transactions on Intelligent Transportation Systems 15:784–793

  7. Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquit Comput 10:255–268

    Article  Google Scholar 

  8. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407–451

    Article  MathSciNet  MATH  Google Scholar 

  9. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15:3736–3745

    Article  MathSciNet  Google Scholar 

  10. Ermagun A (2016) Network econometrics and traffic flow analysis. University of Minnesota

  11. Gao J, Zheng D, Yang S (2019) Sensing the disturbed rhythm of city mobility with chaotic measures: anomaly awareness from traffic flows. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01338-7

  12. González MC, Hidalgo CA, Barabási AL (2008) Understanding individual human mobility patterns. Nature 453:779

    Article  Google Scholar 

  13. Gonzalez H, Han J, Ouyang Y, Seith S (2011) Multidimensional data mining of traffic anomalies on large-scale road networks. Transp Res Rec J Transp Res Board:75–84

  14. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  15. Hu J, Kaparias I, Bell MGH (2009) Spatial econometrics models for congestion prediction with in-vehicle route guidance. IET Intell Transp Syst 3:159–167

    Article  Google Scholar 

  16. Jiang S, Ferreira J, González MC (2012) Clustering daily patterns of human activities in the city. Data Min Knowl Disc 25:478–510

    Article  MathSciNet  MATH  Google Scholar 

  17. Li X, Li Z, Han J, Lee J-G (2009) Temporal outlier detection in vehicle traffic data. In: 2009 IEEE 25th International Conference on Data Engineering. IEEE, pp 1319–1322

  18. Li D, Jiang Y, Rui K, Havlin S (2014) Spatial correlation analysis of cascading failures: Congestions and Blackouts. Sci Rep 4:5381

    Article  Google Scholar 

  19. Mazloumian A, Geroliminis N, Helbing D (2010) The spatial variability of vehicle densities as determinant of urban network capacity. Phil Trans R Soc A 368:4627–4647

    Article  MathSciNet  MATH  Google Scholar 

  20. Meloni S, Gómez-Gardeñes J, Latora V, Moreno Y (2008) Scaling breakdown in flow fluctuations on complex networks. Phys Rev Lett 100:208701. https://doi.org/10.1103/PhysRevLett.100.208701

    Article  Google Scholar 

  21. Min W, Wynter L (2011) Real-time road traffic prediction with spatio-temporal correlations. Transp Res C Emerging Technol 19:606–616

    Article  Google Scholar 

  22. Peng C, Jin X, Wong KC, Shi M, Liò P (2012) Collective human mobility pattern from taxi trips in urban area. PLoS One 7:e34487

    Article  Google Scholar 

  23. Petri G, Expert P, Jensen HJ, Polak JW (2013) Entangled communities and spatial synchronization lead to criticality in urban traffic. Sci Rep 3:1798

    Article  Google Scholar 

  24. Ratti C, Frenchman D, Pulselli RM, Williams S (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plann B Plann Des 33:727–748

    Article  Google Scholar 

  25. Schölkopf B, Platt J, Hofmann T (2006) Sparse Representation for signal classification. In: advances in neural information processing systems 19, Proceedings of the Twentieth Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December. pp 609–616

  26. Smith BL, Williams BM, Oswald RK (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transp Res C Emerging Technol 10:303–321

    Article  Google Scholar 

  27. Sun JB, Yuan J, Wang Y, Si HB, Shan XM (2011) Exploring space–time structure of human mobility in urban space ☆. Phys A Stat Mech Appl 390:929–942

    Article  Google Scholar 

  28. Tang J, Liu F, Wang Y, Wang H (2015) Uncovering urban human mobility from large scale taxi GPS data. Phys A Stat Mech Appl 438:140–153

    Article  Google Scholar 

  29. Trinh HD, Giupponi L, Dini P (2019) Urban anomaly detection by processing mobile traffic traces with LSTM neural networks. In: sensor, mesh and ad hoc communications and networks. pp 1–8

  30. Tucker DH (1965) A representation theorem for a continuous linear transformation on a space of continuous functions. Proc Am Math Soc 16:946–953

    Article  MathSciNet  MATH  Google Scholar 

  31. Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res C 13:211–234

    Article  Google Scholar 

  32. Voort MVD, Dougherty M, Watson S (1996) Combining kohonen maps with arima time series models to forecast traffic flow. Transp Res C Emerging Technol 4:307–318

    Article  Google Scholar 

  33. Wang J, Mao Y, Li J, Xiong Z, Wang WX (2015) Predictability of road traffic and congestion in urban areas. PLoS One 10:e0121825

    Article  Google Scholar 

  34. Wang M, Su Y, Yi S, Gao J (2017) Discovering urban mobility patterns with PageRank based traffic modeling and prediction. Phys A Stat Mech Appl 485:S037843711730465X

    Article  Google Scholar 

  35. Wang J, Wang Y, Zhang D, Lv Q, Chen C (2019) Crowd-powered sensing and actuation in smart cities: current issues and future directions. IEEE Wirel Commun 26:86–92

    Article  Google Scholar 

  36. Wenzhong G, Zhu W, Yu Z, Wang J, Guo B (2019) A survey of task allocation: contrastive perspectives from wireless sensor networks and mobile crowdsensing. IEEE Access 7:78406–78420

    Article  Google Scholar 

  37. Whittaker J, Garside S, Lindveld K (1997) Tracking and predicting a network traffic process. Int J Forecast 13:51–61

    Article  Google Scholar 

  38. Williams BM (2001) Multivariate vehicular traffic flow prediction: evaluation of Arimax modeling

  39. Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transp Eng 129:664–672

    Article  Google Scholar 

  40. Witayangkurn A, Horanont T, Sekimoto Y, Shibasaki R (2013) Anomalous event detection on large-scale gps data from mobile phones using hidden markov model and cloud platform. In: proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication. ACM, pp 1219–1228

  41. Yang S (2013) On feature selection for traffic congestion prediction. Transp Res C 26:160–169

    Article  Google Scholar 

  42. Yang S, Liu W (2011) Anomaly detection on collective moving patterns: a hidden markov model based solution. In: Internet of things (iThings/CPSCom), 2011 international conference on and 4th international conference on cyber, physical and social computing. IEEE, pp 291–296

  43. Yang S, Shi S, Hu X, Wang M (2015) Spatiotemporal context awareness for urban traffic modeling and prediction: sparse representation based variable selection. PLoS One 10:e0141223

    Article  Google Scholar 

  44. Zhang D, Li N, Zhou Z-H, Chen C, Sun L, Li S (2011) iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th international conference on Ubiquitous computing. ACM, pp 99–108

  45. Zhang W, Qi G, Pan G, Lu H, Li S, Wu Z (2015) City-scale social event detection and evaluation with taxi traces. ACM Trans Intell Syst Technol 6:1–20

    Google Scholar 

  46. Zhang M, Li T, Yu Y, Li Y, Hui P, Zheng Y (2020) Urban anomaly analytics: description, detection and prediction. IEEE Trans Big Data 1-1. https://doi.org/10.1109/TBDATA.2020.2991008

  47. Zheng W, Lee DH (2006) Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J Transp Eng 132:114–121

    Article  Google Scholar 

  48. Zhiyong YU, Zheng X, Huang F, Guo W, Sun L, Yu Z (2020) A framework based on sparse representation model for time series prediction in smart city. Front Comput Sci. https://doi.org/10.1007/s11704-11019-18395-11707

  49. Zhou W, Yang S (2011) Outlier detection on large-scale collective behaviors. In: Fourth International Joint Conference on Computational Sciences and Optimization. pp 635–639

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Acknowledgments

Dr. Daqing Zheng is also supported by the funding of SHUFE (No. 2017110433) and Haier Rainforest Project. The authors thank Master Xinjian Zhang for his support in LSTM modeling.

Funding

This work is supported by NSFC (Grant NO. 61472087, and 71301096), Shanghai Science and Technology Commission (Grant No. 17511104203).

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Author notes

  1. The three authors contributed equally to this work.

Authors and Affiliations

  1. Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433, China

    Jun Gao & Su Yang

  2. School of Information Management & Information Systems, Shanghai University of Finance & Economics, Shanghai, 200433, China

    Daqing Zheng

  3. Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance & Economics, Shanghai, China

    Daqing Zheng

Authors
  1. Jun Gao
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  2. Daqing Zheng
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  3. Su Yang
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Correspondence to Daqing Zheng or Su Yang.

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Gao, J., Zheng, D. & Yang, S. Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics. Pers Ubiquit Comput 27, 647–660 (2023). https://doi.org/10.1007/s00779-020-01474-4

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  • DOI: https://doi.org/10.1007/s00779-020-01474-4

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