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Understanding the Intra-urban Residents’ Trip Characteristics Based on GPS
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Understanding the Intra-urban Residents’ Trip Characteristics Based on GPS (2019)Understanding the Intra-urban Residents’ …Understanding bike sharing travel patterns: An analysis of trip data from eight cities (2018)Understanding bike sharing tr…Revealing intra-urban travel patterns and service ranges from taxi trajectories (2017)Revealing intra-urban travel …On the regularity of human mobility (2016)On the regularity of human mo…Revealing travel patterns and city structure with taxi trip data (2015)Revealing travel patterns and…Identifying Travel Patterns During Extreme Weather Using Taxi GPS Data (2015)Identifying Travel Patterns D…Identifying mismatch between urban travel demand and transport network services using GPS data: A case study in the fast growing Chinese city of Harbin (2015)Identifying mismatch between …Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2014)Exploring Universal Patterns …Exploring human mobility with multi-source data at extremely large metropolitan scales (2014)Exploring human mobility with…Understanding intra-urban trip patterns from taxi trajectory data (2012)Understanding intra-urban tri…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Understanding individual human mobility patterns (2008)Understanding individual huma…Revealing travel patterns and city structure with taxi trip dataRevealing travel patterns and…The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)A universal model for mobility and migration patterns (2012)Limits of Predictability in Human Mobility (2010)Uncovering urban human mobility from large scale taxi GPS data (2015)Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method (2016)Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic (2020)Friendship and mobility (2011)Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data (2017)Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic (2021)The Analysis of Spatial Association by Use of Distance Statistics (1992)Power-Law Distributions in Empirical Data (2009)Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China (2017)Power-law distributions in empirical data (2018)Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression (2019)Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories (2020)Central Places in Southern Germany (1967)Difference of urban development in China from the perspective of passenger transport around Spring Festival (2017)Uncovering the spatial structure of mobility networks (2015)African Mining, Gender, and Local Employment (2016)Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China (2018)Incorporating spatial interaction patterns in classifying and understanding urban land use (2015)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)A survey of results on mobile phone datasets analysis (2015)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Human mobility: Models and applications (2018)Filling the gaps (2016)Unveiling large-scale commuting patterns based on mobile phone cellular network data (2020)On estimating the predictability of human mobility: the role of routine (2021)Optimizing bike sharing systems from the life cycle greenhouse gas emissions perspective (2020)Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai (2021)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2015Revealing travel patterns and city structure with taxi trip data408 cited
  • 2014Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data329 cited
  • 2012Understanding intra-urban trip patterns from taxi trajectory data327 cited
  • n.d.Revealing travel patterns and city structure with taxi trip data246 cited
  • 2014Exploring human mobility with multi-source data at extremely large metropolitan scales153 cited
  • 2018Understanding bike sharing travel patterns: An analysis of trip data from eight cities120 cited
  • 2017Revealing intra-urban travel patterns and service ranges from taxi trajectories97 cited
  • 2016On the regularity of human mobility58 cited
  • 2015Identifying mismatch between urban travel demand and transport network services using GPS data: A case study in the fast growing Chinese city of Harbin48 cited
  • 2015Identifying Travel Patterns During Extreme Weather Using Taxi GPS Data8 cited

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