01590105

Component

In this paper, the authors demonstrate the use of an inexpensive and easy-to-collect long-term dataset to address the problems caused by basing activity space studies off short-term data. In total, the authors use 63,114 geo-tagged tweets from 116 unique users to create individuals’ activity spaces based on minimum bounding geometry (convex hull). By using polygon density maps of activity space, the authors found clear differences between weekday and weekend activity spaces, and were able to observe the growth trajectory of activity space over 17 weeks. In order to reflect the heterogeneous nature of spatial behavior and tweeting habits, the authors used Latent Class Analysis twice. First, to identify five unique patterns of location-based activity spaces that are different in shape and anchoring. Second, the authors identify three unique growth trajectories. The comparison among these latent growth trajectories shows that in order to capture the extent of activity spaces the authors need long time periods for some individuals and shorter periods of observation for others. The authors also show that past studies using a single digit number of weeks may not be sufficient to capture individuals’ activity space. The major activity locations identified using a multilevel latent class model, do not appear to be statistically related to the growth patterns of Twitter users activity spaces. The evidence here shows Twitter data can be a valuable complementary source of information for heterogeneity analysis in activity-based modeling and simulation.

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values.

01584066

16-0070

English

20p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Activity choices; Data collection; Days; Social media; Spatial analysis

Twitter

Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2016 Paper #16-0070

TRIS, TRB, ATRI

Jan 12 2016 4:17PM