SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Analyzing Passenger Travel Demand Related to the Transportation Hub inside a City Area using Mobile Phone Data
12 references·5 citing·43 second hop
Analyzing Passenger Travel Demand Related to the Transportation Hub inside a City Area using Mobile Phone Data (2018)Analyzing Passenger Travel Demand Related…Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data (2016)Characterizing Passenger Flow…Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Activity-Based Human Mobility…Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Origin–destination trips by p…Analyzing Cell Phone Location Data for Urban Travel (2015)Analyzing Cell Phone Location…From traces to trajectories: How well can we guess activity locations from mobile phone traces? (2014)From traces to trajectories: …Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Understanding individual mobi…Evaluating long‐distance travel patterns in Israel by tracking cellular phone positions (2011)Evaluating long‐distance trav…Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Estimating Origin-Destination…Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010)Activity-Aware Map: Identifyi…Understanding individual human mobility patterns (2008)Understanding individual huma…Cellular-Based Data-Extracting Method for Trip Distribution (2006)Cellular-Based Data-Extractin…Tracking survey for individual travel behaviour using mobile communication instruments (2004)Tracking survey for individua…Unraveling Influence of Psychological Heterogeneity on Intercity Travelers’ Last-Mile Mode Choice Based on Mixture Regression Modeling (2025)Unraveling Influence of Psych…Understanding tourism travel behavior by combining revealed preference survey and mobile phone data (2025)Understanding tourism travel …Exploring public transport accessibility equity to railway stations under travel time uncertainty (2025)Exploring public transport ac…Trip purpose inference for tourists by machine learning approaches based on mobile signaling data (2021)Trip purpose inference for to…Identifying Human Mobility Patterns in the Rio de Janeiro Metropolitan Area using Call Detail Records (2020)Identifying Human Mobility Pa…Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Limits of Predictability in Human Mobility (2010)Modelling the scaling properties of human mobility (2010)Understanding individual routing behaviour (2016)Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis (2018)Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Reality mining: sensing complex social systems (2005)Correlating mobile phone usage and travel behavior – A case study of Harbin, China (2011)Intra-urban human mobility patterns: An urban morphology perspective (2011)Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach (2015)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)A Theoretical Foundation for the Gravity Equation. (1979)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)A new insight into land use classification based on aggregated mobile phone data (2014)Development of origin–destination matrices using mobile phone call data (2014)Observing the rhythms of daily life: A six-week travel diary (2002)Deriving origin–destination data from a mobile phone network (2007)Uncovering individual and collective human dynamics from mobile phone records (2008)A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures (2014)Closer to the total? Long-distance travel of French mobile phone users (2017)Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata (2018)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data (2015)A data driven method for OD matrix estimation (2019)The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area (2021)Unravelling daily human mobility motifs (2013)The path most traveled: Travel demand estimation using big data resources (2015)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Route Choice: Wayfinding in Transport Networks (1990)Measuring behaviour: an introductory guide (1994)Measuring Behaviour. An Introductory Guide (1994)A Social Network Analysis of Overseas Tourist Movement Patterns in Beijing: the Impact of the Olympic Games (2011)New Approaches to Human Mobility: Using Mobile Phones for Demographic Research (2012)Supporting large-scale travel surveys with smartphones – A practical approach (2013)Examining the impacts of residential self-selection on travel behavior: A focus on methodologies (2007)The universal visitation law of human mobility (2021)Travel and the Built Environment (2010)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)Human mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Measuring inequality in community resilience to natural disasters using large-scale mobility data (2021)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example645 cited
  • 2015Origin–destination trips by purpose and time of day inferred from mobile phone data637 cited
  • 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data511 cited
  • 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore472 cited
  • 2010Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data280 cited
  • 2004Tracking survey for individual travel behaviour using mobile communication instruments196 cited
  • 2015Analyzing Cell Phone Location Data for Urban Travel180 cited
  • 2014From traces to trajectories: How well can we guess activity locations from mobile phone traces?164 cited
  • 2011Evaluating long‐distance travel patterns in Israel by tracking cellular phone positions80 cited
  • 2006Cellular-Based Data-Extracting Method for Trip Distribution63 cited
  • 2016Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data46 cited

Cited by

  • 2021Trip purpose inference for tourists by machine learning approaches based on mobile signaling data14 cited
  • 2020Identifying Human Mobility Patterns in the Rio de Janeiro Metropolitan Area using Call Detail Records

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

14 cited
  • 2025Understanding tourism travel behavior by combining revealed preference survey and mobile phone data5 cited
  • 2025Unraveling Influence of Psychological Heterogeneity on Intercity Travelers’ Last-Mile Mode Choice Based on Mixture Regression Modeling2 cited
  • 2025Exploring public transport accessibility equity to railway stations under travel time uncertainty2 cited