From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity. Issue 10 (2nd October 2020)

• Record Type: Journal Article
• Authors: Naghizade, Elham; Chan, Jeffrey; Tomko, Martin
• Is Part Of: International journal of geographical information science. Volume 34:Issue 10(2020)
• Page Start: 2004
• Page End: 2029
• Publication Date: 2020-10-02
• Subjects: Personalized movement profile -- Trip Diversity -- prefix tree -- GPS trajectory; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1730849
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.