Yingyu Wu

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A new methodology is developed to discover and analyze the hidden knowledge of massive taxi trajectory data within a city. This approach creatively transforms the geographic coordinates (i.e. latitude and longitude) to street names reflecting contextual semantic information. Consequently, the movement of each taxi is studied as a document consisting of the(More)
The Heronian mean provides an aggregation operator that considers the interdependent phenomena among the aggregated arguments. Nevertheless, it seems that the existing literatures only consider the HM for aggregating crisp numbers rather than any other types of arguments. In this paper, the HM under interval-valued intuitionistic fuzzy environments is(More)
Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as "What were the taxi trips starting from Main Street(More)
The Bonferroni mean (BM) can be used in situations where the aggregated arguments are correlated. BM is very useful for solving decision-making problems. For describing fuzziness and vagueness more accurately, the interval-valued hesitant fuzzy set (IVHFS), which is a generalization of the hesitant fuzzy set (HFS), can be used to describe the membership(More)
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