Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data

ISPRS International Journal of Geo-Information - Tập 2 Số 2 - Trang 371-384
Xintao Liu1, Yifang Ban1
1Division of Geoinformatics, Royal Institute of Technology (KTH), Stockholm SE-10044, Sweden

Tóm tắt

In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were selected to generate spatio-temporal clusters, which indicated the typical stop-and-go movement pattern in real-world traffic congestion. We found that the sizes of spatio-temporal clusters exhibited a power law distribution. This implies the presence of a scaling property; i.e., they can be naturally divided into a strong hierarchical structure: long time-duration ones (a low percentage) whose values lie above the mean value and short ones (a high percentage) whose values lie below. The spatio-temporal clusters at different levels represented the degree of traffic congestions, for example the higher the level, the worse the traffic congestions. Moreover, the distribution of traffic congestions varied spatio-temporally and demonstrated a multinuclear structure in urban road networks, which suggested there is a correlation to the corresponding internal mobile regularities of an urban system.

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Tài liệu tham khảo

Dhingra, S.L., and Gull, I. (2008, January 8–10). Traffic Flow Theory Historical Research Perspectives. Proceedings of Traffic Flow Theory and Characteristics Committee (AHB45), Woods Hole, MA, USA.

Greenshields, B.D., and Weids, F.M. (1952). Statistics with Applications to Highway Traffic Analyses, The Eno Foundation for Highway Traffic Control.

Greenberg, H. (1958). A Mathematical Analysis of Traffic Flow, Tunnels and Bridges Department, Project and Planning Division, Port of New York Authority.

1970, What about people in regional science?, Pap. Reg. Sci. Assoc., 24, 6, 10.1007/BF01936872

Haight, 1958, Towards a unified theory of road traffic, Oper. Res., 6, 813, 10.1287/opre.6.6.813

Kalnis, P., Mamoulis, N., and Bakiras, S. (2005, January 22–24). On Discovering Moving Clusters in Spatio-Temporal Data. Proceedings of 9th International Conference on Advances in Spatial and Temporal Databases SSTD, Angra dos Reis, RJ, Brazil.

Kerner, 1999, The physics of traffic, Phys. World, 12, 25, 10.1088/2058-7058/12/8/30

Nagatani, 2002, The physics of traffic jams, Rep. Prog. Phys., 65, 1331, 10.1088/0034-4885/65/9/203

Doulet, 2004, Urban mobility: A new conceptual framework, Urban Plan. Forum, 2, 90

Bogorny, 2009, ST-DMQL: A semantic trajectory data mining query language, Int. J. Geogr. Inf. Sci., 23, 1245, 10.1080/13658810802231449

Spaccapietra, 2008, Conceptual view on trajectories, Data Knowl. Eng., 65, 126, 10.1016/j.datak.2007.10.008

Yan, Z., Parent, C., Spaccapietra, S., and Chakraborty, D. (June, January 30). A Hybrid Model and Computing Platform for Spatio-Semantic Trajectories. Proceedings of 7th Extended Semantic Web Conference (ESWC), Heraklion, Greece.

Hwang, S.Y., Lee, C.M., and Lee, C.H. (2008, January 26–28). Discovering Moving Clusters from Spatial-Temporal Databases. Proceedings of Eighth International Conference on Intelligent Systems Design and Applications (ISDA ’08), Kaohsiung, Taiwan.

Rosswog, J., and Ghose, K. (2008, January 15–19). Detecting and Tracking Spatio-Temporal Clusters with Adaptive History Filtering. Proceedings of IEEE International Conference on Data Mining Workshops ICDM Workshops, Binghamton, NY, USA.

Cao, 2007, Discovery of periodic patterns in spatio-temporal sequences, IEEE Trans. Knowl. Data Eng., 19, 453, 10.1109/TKDE.2007.1002

Bazzani, A., Giorgini, B., Rambaldi, S., Gallotti, R., and Giovannini, L. (2010). Statistical laws in urban mobility from microscopic GPS data in the area of Florence. J. Stat. Mech. Theory Exp.

Hoque, M.A., Hong, X., and Dixon, B. (February, January 30). Analysis of Mobility Patterns for Urban Taxi Cabs. Proceedings of IEEE International Conference on Computing, Networking and Communications (IEEE ICNC), Maui, HI, USA.

Helbing, 2001, Self-organizing pedestrian movement, Environ. Plan. B Plan. Design, 28, 361, 10.1068/b2697

Helbing, 2004, The physics of traffic and regional development, Contemp. Phys., 45, 405, 10.1080/00107510410001715944

Kerner, 1998, Experimental features of self-organization in traffic flow, Phys. Rev. Lett., 81, 3797, 10.1103/PhysRevLett.81.3797

Li, 2011, Using cloud computing to process intensive floating car data for urban traffic surveillance, Int. J. Geogr. Inf. Sci., 25, 1303, 10.1080/13658816.2011.577746

Rozenfeld, 2009, The area and population of cities: New insights from a different perspective on cities, Am. Econ. Rev., 101, 2205, 10.1257/aer.101.5.2205

Clauset, 2009, Power-law distributions in empirical data, SIAM Rev., 51, 661, 10.1137/070710111

Adamic, L.A. Zipf, Power-Laws, and Pareto—A Ranking Tutorial. Available online:http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html.

Adamic, 2011, Unzipping Zipf’s law, Nature, 474, 164, 10.1038/474164a

Jiang, 2011, Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information, Int. J. Geogr. Inf. Sci., 26, 215, 10.1080/13658816.2011.575074

Liu, 2012, A novel approach to the identification of urban sprawl patches based on the scaling of geographic space, Int. J. Geomat. Geosci., 2, 415

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ISPRS International Journal of Geo-Information

Tập 2 Số 2

371-384

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