World Wide Web

, Volume 21, Issue 3, pp 825–847 | Cite as

LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

  • Xiangjie Kong
  • Ximeng Song
  • Feng Xia
  • Haochen Guo
  • Jinzhong Wang
  • Amr Tolba
Part of the following topical collections:
  1. Special Issue on Mobile Crowdsourcing


As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.


Traffic anomaly detection Mobile crowdsourcing Urban big data Anomaly index 



The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP#0078. This work was partially supported by the National Natural Science Foundation of China under Grants no. 61572106, the Natural Science Foundation of Liaoning Province, China under Grants no. 201602154, and the Dalian Science and Technology Planning Project under Grant no. 2015A11GX015 and 2015R054.


  1. 1.
    Batty, M.: Big data, smart cities and city planning. Dialog. Human Geograph. 3(3), 274–279 (2013)CrossRefGoogle Scholar
  2. 2.
    Borg, D.L., Scerri, K.: Efficient traffic modelling and dynamic control of an urban region. Transp. Res. Procedia 6, 224–238 (2015)CrossRefGoogle Scholar
  3. 3.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: Identifying density-based local outliers. SIGMOD 29(2), 93–104 (2000)CrossRefGoogle Scholar
  4. 4.
    Cao, T.T., Edelsbrunner, H., Tan, T.S.: Triangulations from topologically correct digital voronoi diagrams. Comput. Geom. 48(7), 507–519 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3), 1–58 (2009)CrossRefGoogle Scholar
  6. 6.
    Chawla, S., Zheng, Y., Hu, J.: Inferring the root cause in road traffic anomalies. In: ICDM, pp. 141–150. Brussels (2012)Google Scholar
  7. 7.
    Chen, C., Zhang, D., Zhou, Z.H., Li, N., Atmaca, T., Li, S.: B-planner: Night bus route planning using large-scale taxi gps traces. In: 2013 IEEE International Conference on Pervasive Computing and Communications, pp. 225–233. California (2013)Google Scholar
  8. 8.
    Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S., Wang, Z.: iboat: Isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)CrossRefGoogle Scholar
  9. 9.
    Chen, M., Mao, S., Liu, Y.: Big data: A survey. Mob. Netw. Appl. 19(2), 171–209 (2014)CrossRefGoogle Scholar
  10. 10.
    Feng, Z., Zhu, Y., Zhang, Q., Ni, L.M., Vasilakos, A.V.: Trac: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, pp. 1231–1239. Toronto (2014)Google Scholar
  11. 11.
    Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N.Y., Huang, R., Zhou, X.: Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48(1), 7:1–7:31 (2015)CrossRefGoogle Scholar
  12. 12.
    Guo, B., Chen, H., Han, Q., Yu, Z., Zhang, D., Wang, Y.: Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans. Mob. Comput. PP(99), 1–1 (2016)Google Scholar
  13. 13.
    Guo, B., Chen, H., Yu, Z., Nan, W., Xie, X., Zhang, D., Zhou, X.: Taskme: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. Int. J. Human-Comput. Stud. 102(6), 14–26 (2017)CrossRefGoogle Scholar
  14. 14.
    Guo, B., Liu, Y., Wu, W., Yu, Z., Han, Q.: Activecrowd: A framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Trans. Human-Mach. Syst. 47(3), 392–403 (2017)CrossRefGoogle Scholar
  15. 15.
    Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier Detection for Temporal Data, vol. 5 (2014)Google Scholar
  16. 16.
    Huang, C., Wu, X.: Discovering road segment-based outliers in urban traffic network. In: 2013 IEEE Globecom Workshops, pp. 1350–1354. Atlanta (2013)Google Scholar
  17. 17.
    Jin, L., Han, M., Liu, G., Feng, L.: Detecting cruising flagged taxis’ passenger-refusal behaviors using traffic data and crowdsourcing. In: UTC-ATC-ScalCom, pp. 18–25. Bali (2014)Google Scholar
  18. 18.
    Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Futur. Gener. Comput. Syst. 61, 97–107 (2016)CrossRefGoogle Scholar
  19. 19.
    Kong, X., Xia, F., Wang, J., Rahim, A., Das, S.K.: Time-location-relationship combined service recommendation based on taxi trajectory data. IEEE Trans. Indus. Inform. (2017). doi: 10.1109/TII.2017.2684163
  20. 20.
    Kuang, W., An, S., Jiang, H.: Detecting traffic anomalies in urban areas using taxi gps data. Math. Probl. Eng. 2015(2015), 1–13 (2015)CrossRefGoogle Scholar
  21. 21.
    Kumar, G.R., Nimmala, M., Narasimha, G.: An approach for intrusion detection using novel gaussian based kernel function. J. Univ. Comput. Sci. 22(4), 589–604 (2016)MathSciNetGoogle Scholar
  22. 22.
    Li, J., Liu, C., Yu, J.X., Chen, Y., Sellis, T., Culpepper, J.S.: Personalized influential topic search via social network summarization. IEEE Trans. Knowl. Data Eng. 28(7), 1820–1834 (2016)CrossRefGoogle Scholar
  23. 23.
    Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1010–1018. California (2011)Google Scholar
  24. 24.
    Liu, C., Deng, K., Li, C., Li, J., Li, Y., Luo, J.: The optimal distribution of electric-vehicle chargers across a city. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 261–270 (2016)Google Scholar
  25. 25.
    Ma, S., Zheng, Y., Wolfson, O.: T-share: A large-scale dynamic taxi ridesharing service. In: 2013 IEEE 29th International Conference on Data Engineering, pp. 410–421. Brisbane (2013)Google Scholar
  26. 26.
    Mohibullah, M., Hossain, M.Z., Hasan, M.: Comparison of euclidean distance function and manhattan distance function using k-mediods. Int. J. Comput. Sci. Inf. Secur. 13(10), 61 (2015)Google Scholar
  27. 27.
    Mrazovic, P., Matskin, M., Dokoohaki, N.: Trajectory-based task allocation for reliable mobile crowd sensing systems. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 398–406. NJ (2015)Google Scholar
  28. 28.
    Pang, L.X., Chawla, S., Liu, W., Zheng, Y.: On detection of emerging anomalous traffic patterns using gps data. Data Knowl. Eng. 87(9), 357–373 (2013)CrossRefGoogle Scholar
  29. 29.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  30. 30.
    Sun, L., Zhang, D., Chen, C., Castro, P.S., Li, S., Wang, Z.: Real time anomalous trajectory detection and analysis. Mob. Netw. Appl. 18(3), 341–356 (2012)CrossRefGoogle Scholar
  31. 31.
    Zhang, D., Li, N., Zhou, Z.H., Chen, C., Sun, L., Li, S.: ibat: Detecting anomalous taxi trajectories from gps traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99–108. Beijing (2011)Google Scholar
  32. 32.
    Zhang, J.D., Xu, J., Liao, S.S.: Aggregating and sampling methods for processing gps data streams for traffic state estimation. IEEE Trans. Intell. Transp. Syst. 14(4), 1629–1641 (2013)CrossRefGoogle Scholar
  33. 33.
    Zhang, D., Wang, L., Xiong, H., Guo, B.: 4w1h in mobile crowd sensing. IEEE Commun. Mag. 52(8), 42–48 (2014)CrossRefGoogle Scholar
  34. 34.
    Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 89–98. Beijing (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Xiangjie Kong
    • 1
  • Ximeng Song
    • 1
  • Feng Xia
    • 1
  • Haochen Guo
    • 1
  • Jinzhong Wang
    • 1
    • 4
  • Amr Tolba
    • 2
    • 3
  1. 1.The Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Riyadh Community CollegeKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Mathematics Department, Faculty of ScienceMenoufia UniversityShebin El-KomEgypt
  4. 4.School of Management and JournalismShenyang Sport UniversityShenyangChina

Personalised recommendations