Sensing Urban Structures and Crowd Dynamics with Mobility Big Data

  • Yan Liu
  • Longbiao ChenEmail author
  • Linjin Liu
  • Xiaoliang Fan
  • Sheng Wu
  • Cheng Wang
  • Jonathan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)


To facilitate efficient and effective city management, it is important for urban authorities to understand the regular functionalities of urban areas and the irregular crowd dynamics moving around the city. However, existing methods relying on manual surveys and statistics usually cost substantial time and labor, hindering the fine-grain characterization of urban structures and the in-depth understanding of crowd dynamics. In this paper, we leverage large-scale mobility data collected from vehicle GPS devices to analyze the dynamics of crowd movement in different urban areas in a low-cost and automatic manner. We extract the regular crowd movement patterns in different areas, detect the abnormal crowd movement flow peaks, and then interpret the influences of different types of urban events. More specifically, we first divide the city into fine-grained geographic regions and cluster them according to the similarity of crowd movement characteristics. Second, we detect anomaly traffic flow for each cluster area, interpret urban events for each abnormal flow point, and correlate urban events to the interpretation results. Finally, we determine the scope of urban events and use visualization techniques to demonstrate the impact of different types of urban events. We leverage the large-scale real-world datasets from Xiamen City for evaluation. Experimental results validate the effectiveness of our method, and several case studies in Xiamen are conducted.


Crowdsensing Mobility big data Urban computing 



We would like to thank the reviewers for their constructive suggestions. This research was supported by Fujian Collaborative Innovation Center for Big Data Applications in Governments, and the China Fundamental Research Funds for the Central Universities No. 0630/ZK1074, and NSF of China No. U1605254, No. 61371144, No. 61300232.


  1. 1.
    Yuan, N.J., Zheng, Y., Xie, X.: Segmentation of urban areas using road networks. MSR-TR-2012–65, Technical report (2012)Google Scholar
  2. 2.
    Esch, T., Schmidt, M., Breunig, M., et al.: Identification and characterization of urban structures using VHR SAR data. In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1413–1416 (2011)Google Scholar
  3. 3.
    Chen, S., Wu, H., Tu, L., et al.: Identifying hot lines of urban spatial structure using cellphone call detail record data, Ubiquitous Intelligence and Computing. In: 2014 IEEE 11th International Conference on Autonomic and Trusted Computing, and IEEE 14th International Conference on Scalable Computing and Communications and its Associated Workshops (UTC-ATC-ScalCom), pp. 299–304. IEEE (2014)Google Scholar
  4. 4.
    Gonzalez, H., Han, J., Li, X., et al.: Adaptive fastest path computation on a road network: a traffic mining approach. In: Proceedings of the 33rd International Conference on Very Large Data Bases. VLDB Endowment, pp. 794–805 (2007)Google Scholar
  5. 5.
    Krumm, J., Horvitz, E.: Predestination: where do you want to go today. Computer 40(4), 105–107 (2007)CrossRefGoogle Scholar
  6. 6.
    Powell, J.W., Huang, Y., Bastani, F., Ji, M.: Towards reducing taxicab cruising time using spatio-temporal profitability maps. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 242–260. Springer, Heidelberg (2011). Scholar
  7. 7.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2011)Google Scholar
  8. 8.
    Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 155–164. ACM (2012)Google Scholar
  9. 9.
    Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. In: Proceedings of the VLDB Endowment, pp. 980–991 (2012)Google Scholar
  10. 10.
    Liang, Y., Caverlee, J., Cheng, Z., et al.: How big is the crowd?: event and location based population modeling in social media. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 99–108. ACM (2013)Google Scholar
  11. 11.
    Chen, L., Pan, G., Jakubowicz, J., et al.: Complementary base station clustering for cost-effective and energy-efficient cloud-RAN. In: 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017) (2017)Google Scholar
  12. 12.
    Zhang, W., Qi, G., Pan, G., et al.: City-scale social event detection and evaluation with taxi traces. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 40 (2015)Google Scholar
  13. 13.
    Li, H., Ji, H., Zhao, L.: Social event extraction: task, challenges and techniques. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 526–532. IEEE (2015)Google Scholar
  14. 14.
    Ali, S.M.: Time series analysis of Baghdad rainfall using ARIMA method. Iraqi J. Sci. 54, 1136–1142 (2013)Google Scholar
  15. 15.
    Chen, L., Zhang, D., Wang, L., et al.: Dynamic cluster-based over-demand prediction in bike sharing systems. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 841–852. ACM (2016)Google Scholar
  16. 16.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar
  17. 17.
    Veloso, M., Phithakkitnukoon, S., Bento, C.: Urban mobility study using taxi traces. In: Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis, pp. 23–30. ACM (2011)Google Scholar
  18. 18.
    Tostes, A.I.J., de LP Duarte-Figueiredo, F., Assunção, R., et al.: From data to knowledge: city-wide traffic flows analysis and prediction using bing maps. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, p. 12. ACM (2013)Google Scholar
  19. 19.
    Li, H., Wu, Q., Dou, A.: Abnormal traffic events detection based on short-time constant velocity model and spatio-temporal trajectory analysis. J. Inf. Comput. Sci. 10, 5233–5241 (2013)CrossRefGoogle Scholar
  20. 20.
    Wang, L., Zhang, D., Wang, Y., Chen, C., Han, X., M’hamed, A.: Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun. Mag. 54(7), 161–167 (2016)Google Scholar
  21. 21.
    Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. on Syst. Man Cybern. Syst. (TSMC) 45(1), 129–142 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yan Liu
    • 1
  • Longbiao Chen
    • 1
    Email author
  • Linjin Liu
    • 1
  • Xiaoliang Fan
    • 1
  • Sheng Wu
    • 3
  • Cheng Wang
    • 1
  • Jonathan Li
    • 1
    • 2
  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart CitiesXiamen UniversityXiamenChina
  2. 2.WatMos LabUniversity of WaterlooWaterlooCanada
  3. 3.Spatial Information Research Center of FujianFuzhou UniversityFuzhouChina

Personalised recommendations