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Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals
9 references·3 citing·43 second hop
Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals (2017)Usage of Smartphone Data to Derive an Ind…Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index (2014)Repetitions in individual dai…Detecting activity locations from raw GPS data: a novel kernel-based algorithm (2013)Detecting activity locations …Discovering places of interest in everyday life from smartphone data (2012)Discovering places of interes…Can Carpooling Clear the Road and Clean the Air? (2012)Can Carpooling Clear the Road…Predicting human behaviour from selected mobile phone data points (2010)Predicting human behaviour fr…Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…Observing the rhythms of daily life: A six-week travel diary (2002)Observing the rhythms of dail…Data preparation for data mining (1999)Data preparation for data min…The rise and fall of the American carpool: 1970–1990 (1997)The rise and fall of the Amer…A review of travel behavioural pattern using GPS dataset: A systematic literature review (2024)A review of travel behavioura…Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis (2018)Using Passive Data Collection…Using Mobile Phone Data to Observe and Understand Mobility Behavior, Territories, and Transport Usage (2018)Using Mobile Phone Data to Ob…Why Women Work Closer to Home (1981)GENDER DIFFERENCES IN WORK-TRIP LENGTH: EXPLANATIONS AND IMPLICATIONS (1985)Transportation and the Environment (2011)On the morning commute problem with carpooling behavior under parking space constraint (2016)The morning commute problem with ridesharing and dynamic parking charges (2017)Understanding individual human mobility patterns (2008)Understanding mobility based on GPS data (2008)Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015)Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions (2018)Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research (2019)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Learning and inferring transportation routines (2007)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Price-Cost Margins and Market Structure (1976)Moving from monomodality to multimodality? Changes in mode choice of new residents (2017)How many days of global positioning system (GPS) monitoring do you need to measure activity space environments in health research? (2018)Travel behaviour changes under Work-from-home (WFH) arrangements during COVID-19 (2022)Place effects on health: how can we conceptualise, operationalise and measure them? (2002)Understanding and representing ‘place’ in health research: A relational approach (2007)Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study) (2014)Residential buffer, perceived neighborhood, and individual activity space: New refinements in the definition of exposure areas – The RECORD Cohort Study (2016)Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall (2018)Reality mining: sensing complex social systems (2005)Predestination: Inferring Destinations from Partial Trajectories (2006)Eigenbehaviors: identifying structure in routine (2009)A next location prediction method for smartphones using blockmodels (2013)Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering (2013)Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes (2014)CONGESTION THEORY AND TRANSPORT INVESTMENT (1969)Conditional logit analysis of qualitative choice behavior (1972)The Economics of Urban Transportation (2007)Behavioral interventions for sustainable transportation: an overview of programs and guide for practitioners (2019)High Occupancy Vehicle Lanes are an Expected Failure (2019)Transport Challenges in Latin American Cities: Lessons Learnt from Policy Experiences (2019)International encyclopedia of the social & behavioral sciences (2006)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Applying a random forest method approach to model travel mode choice behavior (2018)Exploratory Data Mining and Data Cleaning (2003)Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry (2006)Similarity encoding for learning with dirty categorical variables (2018)Activity spaces: measures of social exclusion? (2003)Unravelling daily human mobility motifs (2013)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)
this work references cited by second hop

References

  • 1999Data preparation for data mining1,363 cited
  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2002Observing the rhythms of daily life: A six-week travel diary503 cited
  • 1997The rise and fall of the American carpool: 1970–1990191 cited
  • 2012Discovering places of interest in everyday life from smartphone data137 cited
  • 2013Detecting activity locations from raw GPS data: a novel kernel-based algorithm134 cited
  • 2014Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index117 cited
  • 2012Can Carpooling Clear the Road and Clean the Air?36 cited
  • 2010Predicting human behaviour from selected mobile phone data points29 cited

Cited by

  • 2024A review of travel behavioural pattern using GPS dataset: A systematic literature review12 cited
  • 2018Using Mobile Phone Data to Observe and Understand Mobility Behavior, Territories, and Transport Usage8 cited
  • 2018Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis5 cited

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