01664113

Component

Traditionally human mobility patterns and space activities are studied using recall-based travel diaries. Following the ubiquity of location-based technologies, transportation researchers are revisiting the methods of classifying travel activity patterns using geo-location data. The current study contributes to this research line by leveraging granular and detailed activity information and building individual lifestyle patterns. The authors used 300 days of 402 Metropia navigation app users’ origin-destination information to construct an activity-user network. Using the co-clustering method, the authors discovered 16 distinct clusters or lifestyles in the dataset. The results of this study indicate: (1) Clustering individuals contingent upon their similar and dissimilar activities enables us to detect their lifestyle, (2) aggregating the activity space of individuals may misrepresent their lifestyles, and consequently mislead the policies, (3) clustering individuals contingent upon their similar and dissimilar activities has the potential to extract the demographic characteristics of individuals, and (4) understanding the mobility patterns of individuals allows for the exploration and possibly creation of social relationships, thereby giving them an opportunity to share their mobility. Moreover, the resulting social structures of this method can be leveraged to form and empower connected and smart communities.

This paper was sponsored by TRB committee ADB50 Standing Committee on Transportation Planning Applications.

18-06375

English

15p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Activity choices; Data analysis; Data files; Navigation systems; Origin and destination; Social media; Travel behavior

Metropia (Software)

Lifestyle

Data and Information Technology; Society; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-06375

TRIS, TRB, ATRI

Jan 8 2018 11:39AM