Abstract
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.
Similar content being viewed by others
References
Batty, M.: Big data, smart cities and city planning. Dialog. Human Geograph. 3(3), 274–279 (2013)
Borg, D.L., Scerri, K.: Efficient traffic modelling and dynamic control of an urban region. Transp. Res. Procedia 6, 224–238 (2015)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: Identifying density-based local outliers. SIGMOD 29(2), 93–104 (2000)
Cao, T.T., Edelsbrunner, H., Tan, T.S.: Triangulations from topologically correct digital voronoi diagrams. Comput. Geom. 48(7), 507–519 (2015)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Chawla, S., Zheng, Y., Hu, J.: Inferring the root cause in road traffic anomalies. In: ICDM, pp. 141–150. Brussels (2012)
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)
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)
Chen, M., Mao, S., Liu, Y.: Big data: A survey. Mob. Netw. Appl. 19(2), 171–209 (2014)
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)
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)
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)
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)
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)
Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier Detection for Temporal Data, vol. 5 (2014)
Huang, C., Wu, X.: Discovering road segment-based outliers in urban traffic network. In: 2013 IEEE Globecom Workshops, pp. 1350–1354. Atlanta (2013)
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)
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)
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
Kuang, W., An, S., Jiang, H.: Detecting traffic anomalies in urban areas using taxi gps data. Math. Probl. Eng. 2015(2015), 1–13 (2015)
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)
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)
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)
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)
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)
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)
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)
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)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
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)
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)
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)
Zhang, D., Wang, L., Xiong, H., Guo, B.: 4w1h in mobile crowd sensing. IEEE Commun. Mag. 52(8), 42–48 (2014)
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)
Acknowledgment
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article belongs to the Topical Collection: Special Issue on Mobile Crowdsourcing
Guest Editors: Bin Guo, Xing Xie, Raghu K. Ganti, Daqing Zhang, and Zhu Wang
Rights and permissions
About this article
Cite this article
Kong, X., Song, X., Xia, F. et al. LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web 21, 825–847 (2018). https://doi.org/10.1007/s11280-017-0487-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-017-0487-4