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Transportation Mode Detection from Low-Power Smartphone Sensors Using Tree-Based Ensembles
15 references·2 citing·42 second hop
Transportation Mode Detection from Low-Power Smartphone Sensors Using Tree-Based Ensembles (2019)Transportation Mode Detection from Low-Po…Review of transportation mode detection approaches based on smartphone data (2017)Review of transportation mode…Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data (2017)Prediction of Individual Soci…Travel Mode Detection with Varying Smartphone Data Collection Frequencies (2016)Travel Mode Detection with Va…Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies (2016)Travel Mode Detection Based o…Stop Detection in Smartphone-based Travel Surveys (2015)Stop Detection in Smartphone-…Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo” (2015)Field Evaluation of the Smart…Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices (2015)Comparison of Travel Diaries …Automatic Trip and Mode Detection with Move Smarter: First Results from the Dutch Mobile Mobility Panel (2015)Automatic Trip and Mode Detec…Automated Transportation Mode Detection Using Smart Phone Applications via Machine Learning: Case Study Mega City of Tehran (2015)Automated Transportation Mode…Urban sensing: Using smartphones for transportation mode classification (2014)Urban sensing: Using smartpho…Transportation mode recognition using GPS and accelerometer data (2013)Transportation mode recogniti…Probabilistic Multimodal Map Matching With Rich Smartphone Data (2013)Probabilistic Multimodal Map …Transportation mode detection using mobile phones and GIS information (2011)Transportation mode detection…Combining GPS, GIS, and Accelerometry: Methodological Issues in the Assessment of Location and Intensity of Travel Behaviors (2010)Combining GPS, GIS, and Accel…Learning transportation mode from raw gps data for geographic applications on the web (2008)Learning transportation mode …Multi-modal Machine Learning Investigation of Telework and Transit Connections (2024)Multi-modal Machine Learning …A Semi-Supervised Deep Residual Network for Mode Detection in Wi-Fi Signals (2020)A Semi-Supervised Deep Residu…Learning and inferring transportation routines (2007)Understanding individual human mobility patterns (2008)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)Cellular Census: Explorations in Urban Data Collection (2007)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Measuring Comfort in Public Transport: A case study for İstanbul (2017)Developing a passive GPS tracking system to study long-term travel behavior (2019)Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data (2019)Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)Mobile Sensing in Environmental Health and Neighborhood Research (2018)Transport mode detection based on mobile phone network data: A systematic review (2019)Inferring High-Level Behavior from Low-Level Sensors (2003)Using GPS to learn significant locations and predict movement across multiple users (2003)Neighborhood Environmental Factors Correlated with Walking Near Home (2006)Active commuting and cardiovascular risk: A meta-analytic review (2007)Search for a global positioning system device to measure person travel (2007)Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review (2013)Big data and understanding change in the context of planning transport systems (2018)Measuring objective and subjective well-being: dimensions and data sources (2020)Tracking survey for individual travel behaviour using mobile communication instruments (2004)Future Mobility Survey (2013)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Closer to the total? Long-distance travel of French mobile phone users (2017)Spatial context mining approach for transport mode recognition from mobile sensed big data (2017)Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics (2017)Understanding transportation modes based on GPS data for web applications (2010)Built Environment Interventions to Increase Active Travel: a Critical Review and Discussion (2019)Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier (2019)Planning Suitable Transport Networks for E-Scooters to Foster Micromobility Spreading (2021)Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement (2010)Inferring fine-grained transport modes from mobile phone cellular signaling data (2019)Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning (2020)Applying mobile phone data to travel behaviour research: A literature review (2017)Inferring transportation modes from GPS trajectories using a convolutional neural network (2017)Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples (2018)Collecting travel diaries: Current state of the art, best practices, and future research directions (2018)Modelling route choice of Dutch cyclists using smartphone data (2018)
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References

  • 2008Learning transportation mode from raw gps data for geographic applications on the web616 cited
  • 2011Transportation mode detection using mobile phones and GIS information451 cited
  • 2013Transportation mode recognition using GPS and accelerometer data235 cited
  • 2014Urban sensing: Using smartphones for transportation mode classification139 cited
  • 2010Combining GPS, GIS, and Accelerometry: Methodological Issues in the Assessment of Location and Intensity of Travel Behaviors137 cited
  • 2016Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies89 cited
  • 2015Automatic Trip and Mode Detection with Move Smarter: First Results from the Dutch Mobile Mobility Panel81 cited
  • 2015Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices80 cited
  • 2016Travel Mode Detection with Varying Smartphone Data Collection Frequencies77 cited
  • 2013Probabilistic Multimodal Map Matching With Rich Smartphone Data71 cited
  • 2015Stop Detection in Smartphone-based Travel Surveys58 cited
  • 2015Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo”44 cited
  • 2017Review of transportation mode detection approaches based on smartphone data40 cited
  • 2017Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

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31 cited
  • 2015Automated Transportation Mode Detection Using Smart Phone Applications via Machine Learning: Case Study Mega City of Tehran30 cited
  • Cited by

    • 2020A Semi-Supervised Deep Residual Network for Mode Detection in Wi-Fi Signals14 cited
    • 2024Multi-modal Machine Learning Investigation of Telework and Transit Connections5 cited