This paper demonstrates the use of non-negative tensor factorization to extract underlying spatio-temporal movement patterns from large-scale urban trajectory data. Individual trajectory data obtained from public transport smart card systems and roadside Bluetooth detectors are represented as a dynamic graph of region-to-region flows to obtain structured data describing flow interactions between regions across time-of-day and day-of-week. Tensor factorization is then applied to these dynamic graphs to characterize traveler movements on different days of the week. The results unveil distinct day-of-week patterns in public transport passenger and roadway vehicle movements, providing insight into the diverse aspects of urban mobility.
Urban Trajectory Analytics: Day-of-Week Movement Pattern Mining Using Tensor Factorization
IEEE Transactions on Intelligent Transportation Systems ; 20 , 7 ; 2540-2549
2019-07-01
3038143 byte
Article (Journal)
Electronic Resource
English
Understanding urban mobility patterns with a probabilistic tensor factorization framework
Online Contents | 2016
|Trajectory Pattern Mining Based on Road Network Detection
British Library Online Contents | 2015
|Controlling Sparseness in Non-negative Tensor Factorization
British Library Conference Proceedings | 2006
|