Authors: Dan Cheng*, George Mason University, Dieter Pfoser, George Mason University
Topics: Geographic Information Science and Systems, Urban Geography
Keywords: Social Media, Twitter, Transportation Network, Movement, Generalization
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Truman, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Social media data become a diverse data source to extract meaningful movement information on individuals. In the past few years, there is an increasing trend using social media data such as Twitter data, Weibo data, and online check-in information to detect the potential movement patterns of individuals. Summarizing travel patterns could help to understand the human-related phenomena such as the spread of infectious disease, prediction of traffic flows and the change of urban landscape. This study aims to extract the human mobility pattern from Twitter data and identify the similar network structure comparing with public transportation networks. Inspired by the map construction algorithms, we propose to generate a location-based network from geo-located Tweets trajectories and identify the potential urban mobility patterns within a city. The result can be applied to predict human movement probabilities and traffic volume on the road network or metro network.