Abstract
Taxi is usually considered as the probe of roads in a city. A large amount of taxi GPS mobility data is able to reflect the human mobility and city traffic. The data is described in spatial and temporal form, from which more information can be mined. One kind of the information is related to the basic statistics of the taxi, such as the taxi id, average/min/max speed, travel distance, load or not etc. Other information such as the taxi’s trajectory or regions of interest in the city can also be obtained. In this paper, we introduce a TaxiCluster to visualize and analyze the taxi data. A procedure on the raw taxi trajectories is introduced in the TaxiCluster.
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References
Al-Dohuki, S., Kamw, F., Zhao, Y., Ma, C., Wu, Y., Yang, J., Li, X., Zhao, Y., Ye, X., Chen, W.: SemanticTraj: a new approach to interacting with massive taxi trajectories. IEEE Trans. Vis. Comput. Graph. 1(1) (2016)
Evans, M., Oliver, D., Shekhar, S., Harvey, F.: Summarizing trajectories into k-primary corridors: a summary of results. In: ACM SIGSPATIAL IWGS, pp. 454–457 (2012)
Guo, D., Zhu, X., Jin, H., Gao, P., Andris, C.: Discovering spatial patterns in origindestination mobility data. Trans. GIS 16(3), 411–429 (2012)
Kumar, D., Wu, H., Lu, Y., Krishnaswamy, S., Palaniswami, M.: Understanding urban mobility via taxi trip clustering. In: IEEE International Conference on Mobile Data Management (2016)
Lee, I., Cai, G., Lee, K.: Exploration of geo-tagged photos through data mining approaches. Expert Syst. Appl. 41(2), 397–405 (2014)
Lee, J., Han, J., Whang, K.: Trajectory clustering: a partitiion-and-group framework. In: SIGMOD, p. 593 (2007)
Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: SmartAdP: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans. Vis. Comput. Graph. 1(1) (2016)
Zhao, Q., Liao, Z., Li, J., Shi, Y., Tang, Q.: A split smart swap clustering for clutter problem in web mapping system. In: WI, pp. 439–443 (2016)
Zhang, J., Qiu, P., Duan, Y., Du, M., Lu, F.: A space-time visualization analysis method for taxi operation in beijing. J. Vis. Lang. Comput. 31, 1–8 (2015)
Zhang, W., Li, S., Pan, G.: Mining the semantics of origin-destination flows using taxi traces. In: ACM Conference on Ubiquitous Computing, pp. 943–949 (2012)
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 (2011)
Zhou, J., Zhao, Q., Li, H.: Integrating time stamps into discovering the places of interest. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 571–580. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09339-0_58
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Xie, M., Zhao, Q. (2017). TaxiCluster: A Visualization Platform on Clustering Algorithms for Taxi Trajectories. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_14
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DOI: https://doi.org/10.1007/978-3-319-69781-9_14
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