01626339

Component

This study reveals the characteristics of travel behavior using high-resolution Twitter data through a series of empirical studies and further explains the abnormal movements extracted from the tweet trajectories. First, this paper explores the characteristics of individual travel behavior especially the location geo-distribution, movement scale and the clustering features of both the directed and undirected travel. Second, this paper proposes a geo-mobility clustering method that groups the tweet locations driven by the same travel motif. This clustering method captures the clustering features of a traveler’s hourly locations and detects the abnormal travel behavior. Third, the tweet posts are examined to identify the social activities behind these abnormal movements. The results of the authors' algorithm show that 46.2% of the abnormal movements can be tied with social activities by the keywords of the tweets.

This paper was sponsored by TRB committee ADB20 Standing Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices.

01618707

17-01782

English

23p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Cluster analysis; Data mining; Location; Social media; Trajectory; Travel behavior; Trip purpose

Twitter

Data and Information Technology; Highways; Planning and Forecasting

Transportation Research Board Annual Meeting 2017 Paper #17-01782

TRIS, TRB, ATRI

Dec 8 2016 10:37AM