SZodixStudyAcademic Library
DashboardShelvesOpen accessMy libraryInboxNotesWriteLearnAlerts
More
Ask your libraryNotificationsQuick citeReview
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study
10 references·5 citing·49 second hop
Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study (2019)Can We Map-Match Individual Cellular Netw…Human mobility: Models and applications (2018)Human mobility: Models and ap…Joint spatial and temporal classification of mobile traffic demands (2017)Joint spatial and temporal cl…CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network (2016)CT-Mapper: Mapping sparse mul…A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas (2016)A Tale of Ten Cities: Charact…High resolution population estimates from telecommunications data (2015)High resolution population es…Development of origin–destination matrices using mobile phone call data (2014)Development of origin–destina…Probabilistic Multimodal Map Matching With Rich Smartphone Data (2013)Probabilistic Multimodal Map …New Approaches to Human Mobility: Using Mobile Phones for Demographic Research (2012)New Approaches to Human Mobil…C-logit stochastic user equilibrium model: formulations and solution algorithm (2010)C-logit stochastic user equil…Understanding individual human mobility patterns (2008)Understanding individual huma…From cell tower location to user location: Understanding the spatial uncertainty of mobile phone network data in human mobility research (2024)From cell tower location to u…Application of cell phone data to monitor attendance during motor racing major event. The case of Formula One Gran Prix in Imola (2024)Application of cell phone dat…Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis (2023)Urban tourists’ spatial distr…Framework for constructing multimodal transport networks and routing using a graph database: A case study in London (2023)Framework for constructing mu…Probabilistic positioning in mobile phone network and its consequences for the privacy of mobility data (2020)Probabilistic positioning in …Learning and inferring transportation routines (2007)Understanding mobility based on GPS data (2008)Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors (2013)Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai (2017)Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data (2020)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)American Apartheid: Segregation and the Making of the Underclass. (1993)Subjective well-being: Three decades of progress. (1999)Ecological Momentary Assessment (2007)Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility (2013)Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared (2014)Urban mobility and neighborhood isolation in America’s 50 largest cities (2018)Nonresponse Rates and Nonresponse Bias in Household Surveys (2006)A universal model for mobility and migration patterns (2012)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)MODELING THE CHOICE OF RESIDENTIAL LOCATION (1978)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)Examining the scaling effect and overlapping problem in logit-based stochastic user equilibrium models (2012)A path-size weibit stochastic user equilibrium model (2013)Optimal joint distance and time toll for cordon-based congestion pricing (2014)OpenStreetMap: User-Generated Street Maps (2008)Dynamic population mapping using mobile phone data (2014)Evidence and future potential of mobile phone data for disease disaster management (2016)Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records (2018)Computational socioeconomics (2019)Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Discovering regions of different functions in a city using human mobility and POIs (2012)Not All Apps Are Created Equal (2017)Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata (2018)TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data (2021)The Varimax Criterion for Analytic Rotation in Factor Analysis (1958)A Rationale and Test for the Number of Factors in Factor Analysis (1965)Evaluating the use of exploratory factor analysis in psychological research. (1999)A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane (2021)A Framework for the Comparative Analysis of Multi-Modal Travel Demand: Case Study on Brisbane Network (2021)Power-Law Distributions in Empirical Data (2009)Power-law distributions in empirical data (2018)The scales of human mobility (2020)The universal visitation law of human mobility (2021)A survey on deep learning for human mobility (2023)Filling the gaps (2016)Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City (2020)Towards a transformation in urban commuting analysis with high-precision mobile phone signaling data: Identifying commuting characteristics based on individual scale (2024)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2018Human mobility: Models and applications1,032 cited
  • 2014Development of origin–destination matrices using mobile phone call data508 cited
  • 2012New Approaches to Human Mobility: Using Mobile Phones for Demographic Research196 cited
  • 2010C-logit stochastic user equilibrium model: formulations and solution algorithm154 cited
  • 2015High resolution population estimates from telecommunications data102 cited
  • 2016A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas100 cited
  • 2013Probabilistic Multimodal Map Matching With Rich Smartphone Data71 cited
  • 2017Joint spatial and temporal classification of mobile traffic demands54 cited
  • 2016CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network25 cited

Cited by

  • 2020Probabilistic positioning in mobile phone network and its consequences for the privacy of mobility data18 cited
  • 2024From cell tower location to user location: Understanding the spatial uncertainty of mobile phone network data in human mobility research15 cited
  • 2023Framework for constructing multimodal transport networks and routing using a graph database: A case study in London14 cited
  • 2023Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

10 cited
  • 2024Application of cell phone data to monitor attendance during motor racing major event. The case of Formula One Gran Prix in Imola7 cited