SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Utilising Mobile Phone Billing Records for TravelMode Discovery
4 references·16 citing·42 second hop
Utilising Mobile Phone Billing Records for TravelMode Discovery (2011)Utilising Mobile Phone Billing Records fo…Transportation mode inference from anonymized and aggregated mobile phone call detail records (2010)Transportation mode inference…Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Understanding individual human mobility patterns (2008)Understanding individual huma…Cellular Census: Explorations in Urban Data Collection (2007)Cellular Census: Explorations…An integrated method for inferring multimodal travel mode choices using mobile network data (2025)An integrated method for infe…Towards a transformation in urban commuting analysis with high-precision mobile phone signaling data: Identifying commuting characteristics based on individual scale (2024)Towards a transformation in u…Travel mode recognition of urban residents using mobile phone data and MapAPI (2021)Travel mode recognition of ur…Mobile phone data in transportation research: methods for benchmarking against other data sources (2021)Mobile phone data in transpor…Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data (2021)Leveraging Individual and Col…Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data (2020)Modelling cellphone trace tra…Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City (2020)Identification of various tra…Modelling long-distance route choice using mobile phone call detail record data: a case study of Senegal (2019)Modelling long-distance route…Modelling departure time choice using mobile phone data (2019)Modelling departure time choi…Approaches, technologies and importance of analysis of the number of train travellers (2019)Approaches, technologies and …Use of mobile phone data for analysis of number of train travellers (2018)Use of mobile phone data for …Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data (2017)Cellpath Routing and Route Tr…Bayesian framework for mobility pattern discovery using mobile network events (2017)Bayesian framework for mobili…CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network (2016)CT-Mapper: Mapping sparse mul…Population Mobility Dynamics Estimated from Mobile Telephony Data (2014)Population Mobility Dynamics …From the Guest Editors: Mobility, Communication, and Urban Space (2014)From the Guest Editors: Mobil…The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)Reality mining: sensing complex social systems (2005)Understanding mobility based on GPS data (2008)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification (2012)Estimating human trajectories and hotspots through mobile phone data (2014)Transport mode detection based on mobile phone network data: A systematic review (2019)Modelling the scaling properties of human mobility (2010)Extracting regular mobility patterns from sparse CDR data without a priori assumptions (2017)Exploring Temporal Activity Patterns of Urban Areas Using Aggregated Network-driven Mobile Phone Data: A Case Study of Wuhu, China (2020)Portraying Citizens’ Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework (2021)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)The New Mobilities Paradigm (2006)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014)A universal model for mobility and migration patterns (2012)Filling the gaps (2016)Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study (2019)Conditional logit analysis of qualitative choice behavior (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimaging (2021)Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China (2021)Deriving transport appraisal values from emerging revealed preference data (2022)THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS (1982)Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model (2001)Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data (2021)Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data (2021)Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory Clustering (2023)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Functional regions as a platform to define integrated transport system zones: The use of population flows data (2022)Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data (2023)Comparison of home detection algorithms using smartphone GPS data (2024)Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Exploring Spatial Nonstationarity in Determinants of Intercity Commuting Flows: A Case Study of Suzhou–Shanghai, China (2022)Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface (2022)Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China (2023)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2010Limits of Predictability in Human Mobility3,230 cited
  • 2007Cellular Census: Explorations in Urban Data Collection406 cited
  • 2010Transportation mode inference from anonymized and aggregated mobile phone call detail records207 cited

Cited by

  • 2021Mobile phone data in transportation research: methods for benchmarking against other data sources34 cited
  • 2014Population Mobility Dynamics Estimated from Mobile Telephony Data33 cited
  • 2016CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network25 cited
  • 2021Travel mode recognition of urban residents using mobile phone data and MapAPI25 cited
  • 2019Modelling long-distance route choice using mobile phone call detail record data: a case study of Senegal24 cited
  • 2019Modelling departure time choice using mobile phone data22 cited
  • 2017Bayesian framework for mobility pattern discovery using mobile network events17 cited
  • 2018Use of mobile phone data for analysis of number of train travellers16 cited
  • 2017Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data14 cited
  • 2019Approaches, technologies and importance of analysis of the number of train travellers

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

11 cited
  • 2020Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data10 cited
  • 2024Towards a transformation in urban commuting analysis with high-precision mobile phone signaling data: Identifying commuting characteristics based on individual scale9 cited
  • 2025An integrated method for inferring multimodal travel mode choices using mobile network data9 cited
  • 2020Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City9 cited
  • 2021Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data9 cited
  • 2014From the Guest Editors: Mobility, Communication, and Urban Space8 cited