Abstract
This study investigates human activity community in a city by conceptualizing it as a network embedding problem. In order to learn the latent representations of activity-travel patterns from individual daily trajectories, network embedding learns a vector space representation for each type of activity place as a node connected by movement links to preserve the structure of individual activities. The proposed approach is applied to mobile positioning data at the individual level obtained for a weekday from volunteers at Guangzhou City. Assessments are conducted to validate individual decision making for several types of activities by a field survey. This study contributes to a general framework for discovering individual activity-travel patterns from human movement trajectories.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
