Cited by

• 2019Inferring fine-grained transport modes from mobile phone cellular signaling data88 cited
• 2020Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning87 cited
• 2019Developing a passive GPS tracking system to study long-term travel behavior80 cited
• 2017Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics76 cited
• 2019Obesity and the Built Environment: A Reappraisal73 cited
• 2020Semi-supervised deep ensemble learning for travel mode identification69 cited
• 2021Review and evaluation of methods in transport mode detection based on GPS tracking data64 cited
• 2022The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland55 cited
• 2023Measuring environmental exposures in people’s activity space: The need to account for travel modes and exposure decay51 cited
• 2018An automated approach from GPS traces to complete trip information47 cited
• 2020Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling44 cited
• 2021GPS data-based mobility mode inference model using long-term recurrent convolutional networks39 cited
• 2019A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data37 cited
• 2021Mobile phone data in transportation research: methods for benchmarking against other data sources34 cited
• 2021A stepwise methodology for transport mode detection in GPS tracking data34 cited
• 2020Are we there yet? Assessing smartphone apps as full-fledged tools for activity-travel surveys33 cited