@article{
 title = {Visual analytics of road traffic with large scale taxi GPS data},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Arrow graph,Cloud computing platform,Continuous encoding,Discrete encoding,Taxi GPS data,Visual analytics of big data},
 volume = {26},
 id = {3d75bf52-88d1-3887-98eb-0fd6910ad0c0},
 created = {2017-12-04T15:05:29.759Z},
 file_attached = {false},
 profile_id = {3b3755e3-945a-3020-9b6d-859d8d29550b},
 last_modified = {2018-01-02T08:33:46.600Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {He2014},
 folder_uuids = {7a48ffb3-7734-4228-9f6f-225ffa5b240c,f92d8429-73be-4980-942e-b74a0df991be},
 private_publication = {false},
 abstract = {©, 2014, Institute of Computing Technology. All right reserved. With large-scale and complicated spatio-temporal characteristics, visual analytics of taxi GPS data is a challenging issue. In this paper, we present a visual analytic method for road traffic analysis based on taxi GPS data, and we adopt two encoding schemes, the discrete arrow graph and the continuous stack graph, to explore the volume, direction, speed and other information of road traffic flow on widened roads based on OpenStreetMap. Douglas-Peucker algorithm and the velocity clustering algorithm are used for data reduction and improving rendering respectively. The preprocessed taxi GPS data are stored in a cloud computing platform in a distributed manner, and MapReduce is utilized to accelerate data and query processing. We test the validities of our proposed encoding schemes on Hangzhou taxi GPS data. Experimental results show that our method can effectively and accurately reveal the status of road traffic in Hangzhou.},
 bibtype = {article},
 author = {He, X. and Sun, G. and Gao, J. and Zheng, C. and Liang, R.},
 journal = {Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics},
 number = {12}
}

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