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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (5) : 145310    https://doi.org/10.1007/s11704-019-9034-z
RESEARCH ARTICLE
Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data
Zhihan JIANG, Yan LIU, Xiaoliang FAN, Cheng WANG, Jonathan LI, Longbiao CHEN()
Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China
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Abstract

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management, while the traditional approaches of which, such as manual surveys, usually incur substantial labor and time. In this paper, we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data. First, we divide the city into fine-grained grids, and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm (DCCA). Second, we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm (ADAM), and correlate them to the urban social and emergency events. Finally, we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city, China, and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

Keywords vehicle mobility      big data      spatial clustering      event detection      urban computing      ubiquitous computing     
Corresponding Author(s): Longbiao CHEN   
Just Accepted Date: 12 July 2019   Issue Date: 10 March 2020
 Cite this article:   
Zhihan JIANG,Yan LIU,Xiaoliang FAN, et al. Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data[J]. Front. Comput. Sci., 2020, 14(5): 145310.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9034-z
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I5/145310
1 Y Zheng. Urban computing: enabling urban intelligence with big data. Frontiers of Computer Science, 2017, 11(1): 1–3
https://doi.org/10.1007/s11704-016-6907-2
2 S Miyazawa, X Song, T Xia, R Shibasaki, H Kaneda. Integrating GPS trajectory and topics from Twitter stream for human mobility estimation. Frontiers of Computer Science, 2019, 13(3): 460–470
https://doi.org/10.1007/s11704-017-6464-3
3 L Wang, D Zhang, Y Wang , C Chen, X Han, A M’hamed. Sparse mobile crowdsensing: challenges and opportunities. IEEE Communications Magazine, 2016, 54(7): 161–167
https://doi.org/10.1109/MCOM.2016.7509395
4 W Zhang, G Qi, G Pan, H Lu, S Li, Z Wu. City-scale social event detection and evaluation with taxi traces. ACM Transactions on Intelligent Systems and Technology (TIST), 2015, 6(3): 40
https://doi.org/10.1145/2700478
5 L Chen, J Jakubowicz, D Yang, D Zhang, G Pan. Fine-grained urban event detection and characterization based on tensor cofactorization. IEEE Transactions on Human-Machine Systems, 2017, 47(3): 380–391
https://doi.org/10.1109/THMS.2016.2596103
6 D Zhang, B Guo, Z Yu. The emergence of social and community intelligence. Computer, 2011, 44(7): 21–28
https://doi.org/10.1109/MC.2011.65
7 C Chen, X Chen, Z Wang, Y Wang, D Zhang. ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints. Frontiers of Computer Science, 2017, 11(1): 61–74
https://doi.org/10.1007/s11704-016-5550-2
8 N J Yuan, Y Zheng, X Xie. Segmentation of urban areas using road networks. MSR-TR-2012–65, Tech. Rep., 2012
9 D Yang, D Zhang, B Qu. Participatory cultural mapping based on collective behavior data in location based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 2016, 7(3): 30
https://doi.org/10.1145/2814575
10 L Wang, D Zhang, D Yang, A Pathak, C Chen, X Han, H Xiong, Y Wang. SPACE-TA: cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing. ACM Transactions Intelligent Systems Technology, 2017, 9(2): 20
https://doi.org/10.1145/3131671
11 D Karamshuk, A Noulas, S Scellato, V Nicosia, C Mascolo. Geospotting: mining online location-based services for optimal retail store placement. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 793–801
https://doi.org/10.1145/2487575.2487616
12 L Chen, X Fan, L Wang, D Zhang, Z Yu, J Li, T M T Nguyen, G Pan, C Wang. RADAR: road obstacle identification for disaster response leveraging cross-domain urban data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 1(4): 130
https://doi.org/10.1145/3161159
13 J Wang , X He, Z Wang, J Wu, N J Yuan, X Xie, Z Xiong. CD-CNN: a partially supervised cross-domain deep learning model for urban resident recognition. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018
14 D Getz. Event Management & Event Tourism. New York: Cognizant Communication Corporation, 1997
15 C Chen, Y Ding, X Xie, S Zhang, Z Wang, L Feng. TrajCompressor: an online map-matching-based trajectory compression framework leveraging vehicle heading direction and change. IEEE Transactions on Intelligent Transportation Systems, 2019
https://doi.org/10.1109/TITS.2019.2910591
16 T Esch, M Schmidt, M Breunig, A Felbier, H Taubenböck, W Heldens, C Riegler, A Roth, S Dech. Identification and characterization of urban structures using VHR SAR data. In: Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. 2011, 1413–1416
https://doi.org/10.1109/IGARSS.2011.6049331
17 S Chen, H Wu, L Tu, B Huang. Identifying hot lines of urban spatial structure using cellphone call detail record data. In: Proceedings of the 11th IEEE International Conference on Ubiquitous Intelligence and Computing, and the 11th International Conference on and Autonomic and Trusted Computing, and the 14th IEEE International Conference on Scalable Computing and Communications and Its Associated Workshops. 2014, 299–304
https://doi.org/10.1109/UIC-ATC-ScalCom.2014.88
18 B Cici, M Gjoka, A Markopoulou, C T Butts. On the decomposition of cell phone activity patterns and their connection with urban ecology. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. 2015, 317–326
https://doi.org/10.1145/2746285.2746292
19 J Krumm, E Horvitz. Predestination: where do you want to go today? Computer, 2007, 40(4): 105–107
https://doi.org/10.1109/MC.2007.141
20 L Chen, D Yang, D Zhang, C Wang, J Li, T M T Nguyen. Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization. Journal of Network and Computer Applications, 2018, 121: 59–69
https://doi.org/10.1016/j.jnca.2018.07.015
21 C Chen, S Jiao, S Zhang, W Liu, L Feng, Y Wang. Triplmputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(10): 3292–3304
https://doi.org/10.1109/TITS.2017.2771231
22 C Li, A Sun, A Datta. Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 155–164
https://doi.org/10.1145/2396761.2396785
23 Y Liang, J Caverlee, Z Cheng, K Y Kamath. 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. 2013, 99–108
https://doi.org/10.1145/2481492.2481503
24 D Yang, D Zhang, L Chen, B Qu. NationTelescope: monitoring and visualizing large-scale collective behavior in LBSNs. Journal of Network and Computer Applications, 2015, 55: 170–180
https://doi.org/10.1016/j.jnca.2015.05.010
25 T Sakaki, M Okazaki, Y Matsuo. Earthquake shakes Twitter users: realtime event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 851–860
https://doi.org/10.1145/1772690.1772777
26 M K Agarwal, K Ramamritham, M Bhide. Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. Proceedings of the VLDB Endowment, 2012, 5(10): 980–991
https://doi.org/10.14778/2336664.2336671
27 Z Yu, D Zhang, Z Wang, B Guo, I Roussaki, K Doolin, E Claffey. Toward context-aware mobile social networks. IEEE Communications Magazine, 2017, 55(10): 168–175
https://doi.org/10.1109/MCOM.2017.1700037
28 X Han, L Wang, R Farahbakhsh, Á Cuevas, R Cuevas, i N Cresp, L He. CSD: a multi-user similarity metric for community recommendation in online social networks. Expert Systems with Applications, 2016, 53: 14–26
https://doi.org/10.1016/j.eswa.2016.01.003
29 A I J Tostes, F de LP Duarte-Figueiredo, R Assunç ao, J Salles, A A Loureiro. 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. 2013, 12
https://doi.org/10.1145/2505821.2505831
30 L Chen, D Zhang, L Wang, D Yang, X Ma, S Li, Z Wu, G Pan, T M T Nguyen, J Jakubowicz. Dynamic cluster-based over-demand prediction in bike sharing systems. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016, 841–852
https://doi.org/10.1145/2971648.2971652
31 S M Ali. Time series analysis of Baghdad rainfall using ARIMA method. Iraqi Journal of Science, 2013, 54(5): 1136–1142
32 H Li, Q Wu, A Dou. Abnormal traffic events detection based on shorttime constant velocity model and spatio-temporal trajectory analysis. Journal of Information and Computational Science, 2013, 10(16): 5233–5241
https://doi.org/10.12733/jics20102439
33 F T Liu, K M Ting, Z H Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): 3
https://doi.org/10.1145/2133360.2133363
34 Z Jiang, Y Liu. Visualization platform. GitHub Website, 2019
35 D Wong. The modifiable areal unit problem (MAUP). The SAGE Handbook of Spatial Analysis, 2009, 105: 23
https://doi.org/10.1016/B978-008044910-4.00475-2
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