TY - JOUR
T1 - Supporting Serendipitous Social Interaction Using Human Mobility Prediction
AU - Yu, Zhiwen
AU - Wang, Hui
AU - Guo, Bin
AU - Gu, Tao
AU - Mei, Tao
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict "serendipitous" social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data from people' daily life on campus and use it as empirical mobility traces to generate decision trees and model trees to predict next venues, arrival times, and user encounter. Mobility regularities are mainly considered in these prediction models, and mobility contexts (e.g., time, location, and speed) act as decision nodes in the classification trees. Experimental results using collected GPS data showed that our system achieves 90% accuracy for predicting a user's next venue using a decision tree algorithm, with minute-level (around 5 min) prediction error for arrival time using the model tree algorithm. Two prototype applications were developed to support serendipitous social interaction on campus, and the feedback from a user study with 25 users demonstrated the usability of these two applications.
AB - Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict "serendipitous" social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data from people' daily life on campus and use it as empirical mobility traces to generate decision trees and model trees to predict next venues, arrival times, and user encounter. Mobility regularities are mainly considered in these prediction models, and mobility contexts (e.g., time, location, and speed) act as decision nodes in the classification trees. Experimental results using collected GPS data showed that our system achieves 90% accuracy for predicting a user's next venue using a decision tree algorithm, with minute-level (around 5 min) prediction error for arrival time using the model tree algorithm. Two prototype applications were developed to support serendipitous social interaction on campus, and the feedback from a user study with 25 users demonstrated the usability of these two applications.
KW - GPS data
KW - inference model
KW - mobility prediction
KW - serendipitous social interaction
KW - user study
UR - http://www.scopus.com/inward/record.url?scp=84959575463&partnerID=8YFLogxK
U2 - 10.1109/THMS.2015.2451515
DO - 10.1109/THMS.2015.2451515
M3 - 文章
AN - SCOPUS:84959575463
SN - 2168-2291
VL - 45
SP - 811
EP - 818
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 6
M1 - 7160746
ER -