Cited by

• 2021Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA31 cited
• 2016Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation28 cited
• 2021An App-Assisted Travel Survey in Official Statistics: Possibilities and Challenges26 cited
• 2021Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model23 cited
• 2020Understanding Human Activity and Urban Mobility Patterns From Massive Cellphone Data: Platform Design and Applications23 cited
• 2017Trip chain extraction using smartphone-collected trajectory data19 cited
• 2022The Smartphone-Based Person Travel Survey System: Data Collection, Trip Extraction, and Travel Mode Detection17 cited
• 2024Activity type detection of mobile phone data based on self-training: Application of the teacher–student cycling model13 cited
• 2021Generating a synthetic probabilistic daily activity-location schedule using large-scale, long-term and low-frequency smartphone GPS data with limited activity information12 cited
• 2022Machine learning for activity pattern detection12 cited
• 2022Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data11 cited
• 2021Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data8 cited
• 2023Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network7 cited
• 2019Individual Travel Transportation Modes Identification Based on Deep Learning Algorithm of Attention Mechanism7 cited
• 2023Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type7 cited
• 2020Statistical characteristics and transportation mode identification of individual trajectories5 cited