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ArticleSimilarity analysis of frequent sequential activity pattern mining

Xuan Sharon Di2018

Article2018

Similarity analysis of frequent sequential activity pattern mining

45 citations·0/0 files stored·Transportation Research Part C Emerging Technologies

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Human Mobility and Location-Based Analysis Data Mining Algorithms and Applications Data Management and Algorithms

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References

2008Understanding individual human mobility patterns 2010Limits of Predictability in Human Mobility 2011Friendship and mobility 2011Smart card data use in public transit: A literature review 2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2013Unravelling daily human mobility motifs 2016The promises of big data and small data for travel behavior (aka human mobility) analysis 2015The path most traveled: Travel demand estimation using big data resources

Cited by

2021Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data 2019Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data 2023Understanding Residents’ Behavior for Smart City Management by Sequential and Periodic Pattern Mining 2021Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data 2019A comparison of time-use behavior in metropolitan areas using pattern recognition techniques

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Details

Year2018

Typearticle

Published2018-09-22

VenueTransportation Research Part C Emerging Technologies

ISSN-L0968-090X

Languageen

Citations45

Files0 public / 0

ShelfHuman Mobility and Location-Based Analysis

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doi10.1016/j.trc.2018.09.018

openalex_workW2891464239

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2024Understanding factors associated with individuals’ non-mandatory activities using machine learning and SHAP interpretation: A case study of Guangzhou, China

2024Exploring human mobility: a time-informed approach to pattern mining and sequence similarity

2022Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities