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Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study
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Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study (2023)Analyzing Urban Mobility Based on Smartph…A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study (2022)A Cluster-Based Approach Usin…The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)The PRISMA 2020 statement: an…Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data (2020)Understanding Individual Mobi…Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai (2020)Role of Big Data in the Devel…Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management (2019)Applying Big Data Analytics t…Understanding Human Mobility Flows from Aggregated Mobile Phone Data (2018)Understanding Human Mobility …Ensemble-Spotting: Ranking Urban Vibrancy via POI Embedding with Multi-view Spatial Graphs (2018)Ensemble-Spotting: Ranking Ur…Urban Mobility Analysis With Mobile Network Data: A Visual Analytics Approach (2017)Urban Mobility Analysis With …Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data (2012)Extracting Dynamic Urban Mobi…WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study (2016)Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data (2017)Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities (2018)A Measurement Theory for Time Geography (2004)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Efficient Similarity Search for Travel Behavior (2018)User Community Identification Through Fine-Grained Mobility Records for Smart City Applications (2020)A survey of results on mobile phone datasets analysis (2015)Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches (2021)Understanding the urban mobility community by taxi travel trajectory (2021)Walkability Indicators in the Aftermath of the COVID-19 Pandemic: A Systematic Review (2022)Real Estate Ranking via Mixed Land-use Latent Models (2015)Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes (2017)Spotting Trip Purposes from Taxi Trajectories (2017)Geographical and temporal huff model calibration using taxi trajectory data (2020)MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems (2021)Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective (2021)Evaluating passive mobile positioning data for tourism surveys: An Estonian case study (2007)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm (2014)Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability (2019)Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection (2021)On a Certain Research Gap in Big Data Mining for Customer Insights (2021)Applied Spatial Statistics for Public Health Data (2004)Social media analytics: a survey of techniques, tools and platforms (2014)Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data (2021)Spatial-temporal analysis of retail and services using Facebook Places data: a case study in Brno, Czech Republic (2021)The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City (2021)Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data (2023)Clustering daily patterns of human activities in the city (2012)Uncovering urban human mobility from large scale taxi GPS data (2015)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data (2021)LTP-Net: Life-Travel Pattern Based Human Mobility Signature Identification (2023)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic (2021)PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews (2021)PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews (2021)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Characterizing the speed and paths of shared bicycle use in Lyon (2010)Mobility Modeling and Prediction in Bike-Sharing Systems (2016)A Demand-Centric Repositioning Strategy for Bike-Sharing Systems (2022)Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models (2023)Assessment of the Energy Efficiency of Individual Means of Transport in the Process of Optimizing Transport Environments in Urban Areas in Line with the Smart City Idea (2025)
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References

  • 2021The PRISMA 2020 statement: an updated guideline for reporting systematic reviews107,380 cited
  • 2012Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data112 cited
  • 2017Urban Mobility Analysis With Mobile Network Data: A Visual Analytics Approach59 cited
  • 2019Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management50 cited
  • 2018Understanding Human Mobility Flows from Aggregated Mobile Phone Data32 cited
  • 2020Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai29 cited
  • 2018Ensemble-Spotting: Ranking Urban Vibrancy via POI Embedding with Multi-view Spatial Graphs28 cited
  • 2022A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study19 cited
  • 2020Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data14 cited

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